Evolution and Impact of Wi-Fi Technology and Applications: A Historical Perspective

  • Published: 19 November 2020
  • Volume 28 , pages 3–19, ( 2021 )

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  • Kaveh Pahlavan 1 &
  • Prashant Krishnamurthy 2  

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The IEEE 802.11 standard for wireless local area networking (WLAN), commercially known as Wi-Fi, has become a necessity in our day-to-day life. Over a billion Wi-Fi access points connect close to hundred billion of IoT devices, smart phones, tablets, laptops, desktops, smart TVs, video cameras, monitors, printers, and other consumer devices to the Internet to enable millions of applications to reach everyone, everywhere. The evolution of Wi-Fi technology also resulted in the first commercial piloting of spread spectrum, high speed optical communications, OFDM, MIMO and mmWave pulse transmission technologies, which then became more broadly adopted by cellular phone and wireless sensor networking industries. The popularity and widespread Wi-Fi deployment in indoor areas further motivated innovation in opportunistic cyberspace applications that exploit the ubiquitous Wi-Fi signals. The RF signal radiated from Wi-Fi access points creates an “RF cloud” accessible to any Wi-Fi equipped device hosting or supporting these opportunistic applications. Wi-Fi positioning and location intelligence were the first popular opportunistic applications of Wi-Fi’s RF cloud. Today, researchers are investigating opportunistic applications of Wi-Fi signals for gesture and motion detection as well as authentication and security. This paper provides a holistic overview of the evolution of Wi-Fi technology and its applications as the authors experienced it in the last few decades.

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1 Introduction

In the last few decades, as we were witnessing the emergence of the “information age” and the third industrial revolution, wireless access and localization played an undisputable role by enabling millions of innovative and popular cyberspace applications to connect to the Internet by anyone, anywhere Footnote 1 . These cyberspace applications have had and continue to make a fundamental impact on the way we live, conduct business, shop, access news media, deliver education, transport, care for health, and interact with the world. Today, smart phones, tablets and laptops use wireless technology to support untethered access to information, which is the most essential part of the way we live and work. Footnote 2 Smart cities monitor the environment and cyberspace intelligence is helping us as a society to optimize the way our intelligence contributes to the collective intelligence of humanity, to optimize the efficiency of consuming resources while sustaining life on the earth. The backbone of this third industrial revolution has been the computer and communication industry. The exponential growth in computational speed and the size of memory for information processing and storage has enabled implementation of numerous cyberspace applications that at the same time demand high speed communications and networking of devices, especially with tetherless connections to easily reach the numerous types of consumer devices emerging with the growth of the microelectronics industry at a reasonable cost. Wireless technologies have played an extremely important role in enabling this revolution to take place and to facilitate the access and intelligent processing of information to anyone, anytime, and anywhere.

At the time of this writing we have two different wireless data interfaces to connect a smartphone to the Internet, IEEE 802.11 wireless local area networks, commercially known as Wi-Fi, and cellular mobile data networks. Wi-Fi is the primary choice for smartphones because it can provide a higher data rate and more reliable indoor connections at a lower cost - users typically resort to cellular networks as a second choice. Researchers in next generations of cellular industry believe that as the cellular data rates and cellular costs goes down, the balance may shift to cellular networks because it is available everywhere. However, as of today Wi-Fi is the fastest and most cost-effective way of wireless Internet connectivity, especially in large parts of the world where broadband wired connectivity exists, but the latest 5G cellular technology does not. In addition to smartphones, many other devices like home entertainment systems, environmental monitoring sensors, and security systems connect to the Internet with Wi-Fi, but not necessarily through cellular networks. Wi-Fi brings people, processes, data, and devices, together and turns data into valuable information that makes life better and business thrive [ 1 ]. Some companies engaged in Wi-Fi related business resort to artistic illustrations similar to Fig.  1 (adapted from [ 2 , 3 ]) to relate Wi-Fi to human basic needs using Maslow’s hierarchy of human needs [ 4 ], with an additional lowest layer called Wi-Fi. Usually, Maslow’s hierarchy is shown as a pyramid, but to illustrate the crucial importance of Wi-Fi, in Fig.  1 the hierarchy is shown using the inverted version of the common symbol for Wi-Fi signal strength with Wi-Fi as the “most basic” of human needs.

figure 1

adapted from [ 2 , 3 ]

Maslow’s hierarchy of human needs with an additional layer referring to Wi-Fi as enabler of these needs

Innovations after the first and second industrial revolution, such as the steam engine, the internal combustion engine, electricity, the telegraph and the telephone, radio, television, airplane, and rockets, had profound impacts on the way we live and have affected many other industries (such as entertainment). However, the Internet, the fruit of the third industrial revolution, enabling the emergence of the “information age”, has had a wider impact on our daily lives. The Internet provides access to unlimited amounts of information in an almost instant manner, anywhere, and that is further enhanced by wireless technologies by allowing devices to be anywhere. Indeed, Wi-Fi is the most popular of the wireless technologies to connect the devices and carry the internet protocol (IP) traffic.

As mentioned above, Wi-Fi is one of two primary wireless technologies that carries IP traffic. The IP traffic includes text, voice, images, and videos that comprises the communication needs in our daily lives and it is a good measure of information exchange on the Internet. A reliable source for measurement and prediction of IP traffic is the Cisco Visual Networking Index: Global Mobile Data Traffic Forecast [ 5 ]. Figure  2 , adapted from this source, shows the breakdown of this data from Mobile, Wi-Fi and Fixed access in different years. We use their prediction of traffic in 2022 as a measure to demonstrate the role of Wi-Fi in handling IP traffic. The traffic is divided into wireless (Wi-Fi and cellular mobile) and wired (Ethernet) with wireless carrying 70.6% and wired carrying 29.4%. Because of its flexibility of connection, being available anywhere, wireless traffic is more than twice the wired traffic. Fixed devices generate 58% of the traffic and mobile devices generate 42%. Wi-Fi carries 22.9% of the traffic from mobile devices (that also have a cellular connection) and 28.1% of traffic from Wi-Fi only devices for a total 51% of the entire traffic. This means that by the year 2022, Wi-Fi may carry the majority of IP global traffic soaring to reach the unbelievable high value of zettabytes (10 21 bytes). The reason for the success of Wi-Fi over wired Ethernet, carrying 29.2% of the traffic, is Wi-Fi’s connection flexibility, and the reason for success over cellular, carrying 19.6% of traffic, is Wi-Fi’s higher speed and less expensive connection cost. We use these numbers as a proxy metric to now explain why we need Wi-Fi. This discussion clarifies our “artistic expression” in the beginning of this section, about the impact of Wi-Fi in our daily needs, in a broader context with historical and projected usage numbers.

figure 2

adapted from [ 5 ]

Approximate global monthly IP traffic for different Internet access methods in 2017 and 2022 (note Exabyte is 10 18 bytes); Data

In this brief introduction we provided our view on the importance and impact of Wi-Fi technology. In the remainder of this paper we provide a holistic overview of the evolution of Wi-Fi technology and its applications. This is a huge area and it is very difficult to write a paper that includes every aspect of its history. As members of a pioneering research center in this field [ 6 ], we provide a historical perspective of evolution of Wi-Fi in the way that we experienced it in the past few decades (and the paper is driven by this personal lens). We approach this challenging task from three angles. First, how the physical (PHY) and medium access control (MAC) for wireless communications with Wi-Fi technology evolved and what were the novel wireless transmission technologies that were introduced in this endeavor. Second, how Wi-Fi positioning emerged as the most popular positioning technology in indoor and urban areas and how it has impacted our daily lives. Third, how other cyberspace applications, such as motion and gesture detection as well as authentication and security, are emerging to revolutionize human computer interfacing with the RF cloud of Wi-Fi devices.

2 Evolution of Wi-Fi Communications Technology

In this section we first discuss the origins of the PHY and MAC layer technologies for Wi-Fi by separating their origins into three eras: (i) prior to 1985, when the pioneering technologies for WLAN were invented, (ii) during the period 1985–1997, when IEEE 802.11 and Wi-Fi technology became IEEE standards and finally, (iii) from 1997 to the present, when orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) technologies enabled Wi-Fi to enhance the supported data rates from 2 Mbps to Gbps. Then we discuss the evolution of the market and the applications of Wi-Fi that eventually emerged.

2.1 The Origin of WLAN Technologies Before 1985

Around the year 1980, IBM‘s Rueschlikon Laboratory, Zurich, Switzerland began research and development on using InfraRed (IR) technology to design WLANs for manufacturing floors [ 7 ]. At that time, wired local area networks (LAN)s were popular in office areas and large manufacturers such as General Motors (GM) were considering their use in computerized manufacturing floors. To wire the inside of offices, it was necessary to snake wires in walls and was easier with low-height suspended ceilings. In manufacturing floors, there are limited numbers of partitioning walls and moreover ceilings are high and made of hard material. Consequently, WLAN technology offered itself as a practical alternative for manufacturing floors. Around the same time frame, HP Palo Alto Laboratory, California, reported a prototype WLAN using direct sequence spread spectrum (DSSS) with surface acoustic wave (SAW) devices (for implementation of a matched filter at the receiver) [ 8 ]. HP Laboratories at that time had open space offices without partitioning by walls, which again created challenges for wiring through the walls. Dropping wires from the ceiling to desktops was not aesthetically pleasing. It was in this time that optical wireless and spread spectrum, combined with spread spectrum technology emerged, that could support huge amounts of capacity for indoor WLANs to connect desktops and printers together in a local network [ 9 , 10 ].

Prior to all this, Norm Abramson at University of Hawaii had designed the first experimental wireless data network, the ALOHA system [ 11 ]. The difference between the approach taken by IBM or HP and this approach was that ALOHA was an academic experimentation of wireless packet data networks with an antenna deployed outdoors with relatively low data rate modems at a speed of around 9600 bps. However, the concept had inspired low speed wireless data networking technologies such as Motorola’s ARDIS, and Ericsson’s Mobitex, (which we refer to as mobile data services now generally subsumed by cellular data services in 3G, 4G, and beyond). For WLAN technologies, the antenna is installed indoors, and the data rate needed to be at least 1 Mbps (at that time) to be considered by IEEE 802 standards organization community as a LAN [ 12 ]. The medium access control of both wireless data services was contention based, originally experimented in ALOHA, and later on evolving into carrier-sensing or listen before talk based contention access adopted by wired LANs by the IEEE 802.3 standard, commercially known as the Ethernet with some variation.

The main obstacle for commercial implementation of the early WLANs were interference and availability of a low cost wideband spectrum in which the WLAN could operate. Indoor optical WLANs did not need to consider regulation by the Federal Communications Commission (FCC) and they could potentially provide extremely wide bandwidths. However, optical communications cannot penetrate walls or other obstacles and thus, the operation becomes restricted to open areas, which are often small inside buildings. Spread spectrum was an anti-interference technology, which at that time could potentially manage the interference problem allowing multiple users to share a wideband spectrum [ 9 , 10 , 13 ]. In the summer of 1985, Mike Marcus, the chief engineer at FCC at that time, released the unlicensed Industrial, Scientific, and Medical (ISM) bands with restrictions of having to use spread spectrum technology for interference management [ 14 ]. For WLANs to become a commercial product, there was need for large bandwidths (at that time) and modem technologies that could overcome the challenges of indoor RF multipath propagation to achieve data rates beyond 1 Mbps required to be considered by the IEEE 802 committee as a LAN. The ISM bands and spread spectrum technology could address both issues.

2.2 Evolution of WLAN Technologies and Standards Between 1985 and 1997

The summer of 1985 was a turning point in the entrepreneurship for implementation in the WLAN industry. The FCC had released the ISM bands for commercial implementation of low power spread spectrum technology in May [ 14 ]. The suitability of RF spread spectrum and IR for implementing wireless office information networks had captured the cover page of the IEEE Communication Magazine in June [ 10 ]. Suddenly, several startup companies and a few groups in large companies, almost exclusively in North America, emerged to begin developing WLANs using spread spectrum and infrared technologies. Infrared devices did not need FCC regulations because they operate above the 300 GHz frequency, the highest governed by this agency. Among the exceptions for locations of these companies, was a small group in NCR, Netherlands, which designed the first Direct Sequence Spread Spectrum (DSSS) technology to achieve 2 Mbps [ 15 , 16 ]. Other companies, such as Proxim, Mountain View, CA, resorted to Frequency Hopping Spread Spectrum (FHSS), and a third group led by Photonics, supported by Apple, resorted to an IR solution for WLANs. All three groups could achieve 2 Mbps. These three groups, originally started in the late 1980’s, laid the foundation for the first legacy IEEE 802.11 standards series, finalized in 1997. The final standard for DSSS and FHSS operated in the 2.4 GHz ISM bands. Other exceptions in technologies were efforts by Motorola, IL and WINDATA, Marlborough, MA. Motorola introduced a revolutionary WLAN technology operating in the 18 GHz licensed bands achieving 10 Mbps using a six sectored antenna configuration [ 17 , 18 ], and WINDATA, achieved 6 Mbps in a dual band mode with 2.4 GHz for uplink and 5.2 GHz for downlink. The work presented in [ 17 , 18 ] was the first time a wireless network was adopting frequencies in 18 GHz with directional antennas. We may refer to this work as the first attempt to use close to mmWave technology for modern wireless networking, which is now adopted by 5G cellular networks [ 19 ]. However, mmWaves cannot penetrate walls well, which restricts their coverage in indoor areas. This restriction is less of a concern in cellular networks with outdoor antenna deployments.

The first academic research in the physical layer of WLANs began at the Worcester Polytechnic Institute, Worcester, MA in Fall of 1985 [ 6 ]. The early academic research literature in this area began with the empirical modeling of the multipath radio propagation in indoor areas [ 20 , 21 , 22 , 23 , 24 , 25 ], examining decision feedback equalization (DFE) [ 26 ], and M-ary orthogonal coding [ 27 ] to achieve data rates beyond the 2 Mbps rates studied by the IEEE 802.11, to achieve rates on the orders of 20 Mbps, and the integration of voice and data for WLAN [ 28 ]. A form of M-ary orthogonal signaling was adopted by IEEE802.11b standard, DFE was adopted by the Pan European HIgh PERformance LAN (HIPERLAN)-I standard, and a form of OFDM was implemented in HIPERLAN-2 and IEEE 802.11a standards. A breakthrough, patented in this era, was the application of OFDM to WLANs, first filed by the Commonwealth Scientific and Industrial Research Organization (CSIRO), Sydney, Australia [ 29 ]. The origins of equalization, quadrature amplitude modulation (QAM), and OFDM transmission technologies were first implemented for commercial voice band communications [ 30 ]. The use of DFE was first adopted for wireless data communications over multipath troposcatter channels in military applications [ 31 ] and M-ary orthogonal coding was an extension to Code Division Multiple Access (CDMA) to increase the capacity for military applications [ 32 ]. The novelty of these technologies in this later time was in their application in commercial WLANs in non-wired and non-military applications.

The IEEE group for WLAN standardization was first formed as IEEE 802.4L in 1998. The IEEE 802.4 group was devoted to Token Bus LANs for manufacturing environments and was on the verge of disbanding. The rationale for introducing WLANs in this group was that new IEEE standards usually begin in a closely related standard and after going through the establishment procedure, may form their own group and standard series. The IEEE 802.11 group was the same group from IEEE 802.4L formed later in July 1990 [ 33 ]. In the early days of this standard the important challenge was to find the correct direction for the future technology among a number of acceptable technologies. In 1991, the standards group participated in the first IEEE sponsored conference to address this issue [ 34 ]. The early IEEE standards for wired LANs were differentiating from each other through their MAC method. IEEE 802.11 was the first with three MAC mechanisms (that could work together) and three PHY layer methods. The legacy IEEE 802.11 standard was completed in 1997 with three PHY layer recommendations, DSSS and FHSS operating in the 2.4 GHz ISM bands and Diffused Infrared (DFIR) wireless optical options. All three PHY layer options operated at raw data rate options of 1 and 2 Mbps and employed three possibilities for the MAC: carrier sensing - CSMA, request and clear to send - RTS-CTS, and polling (point-coordination-function) - PCF. RTS/CTS and PCF were designed to operate in conjunction with the base CSMA with collision avoidance (CSMA/CA).

The HIPERLAN was another WLAN standardization activity, sponsored by the European Telecommunications Standards Institute (ETSI), which began its work in 1992. The HIPERLAN-1 standard was the first attempt to achieve data rates above 10Mbps using DFE technology and in the 5 GHz unlicensed bands [ 35 ]. This standard was also completed in 1997, but it failed in developing a market for itself. Another more popular but extensive standardization activity for wireless indoor networking in this era was Wireless Asynchronous Transfer Mode (W-ATM), which aimed to integrate local wireless traffic with an ATM backbone wired technology [ 36 , 37 ]. A comparison of this technology with Wi-Fi is available in [ 38 ].

In summary, it is fair to say that during the 1985–1997 era, the WLAN industry was in the process of discovering technologies for wideband indoor wireless communications and it examined spread spectrum, M-ary orthogonal coding, IR, licensed bands at 18 GHz with directional antennas, DFE, and OFDM technologies and the importance of the analysis of the effects of multipath and appropriate mitigation techniques to achieve higher data rates. The spread spectrum and Infrared technologies of the legacy IEEE 802.11 standard were the only technologies which survived in the market and a modified form of these technologies have remained in other popular standards such as Bluetooth, using FHSS and ZigBee using DSSS. M-ary orthogonal coding and OFDM appeared in later standards. This era also opened channels for dissemination of research and scholarship through publication channels. The first IEEE workshop on WLAN (1991), and the IEEE International Symposium on Personal, Indoor, and Mobile Communications (1992), hosted first panel discussions on the future of the WLAN industry in cooperation with the IEEE 802.11 standardization organization. The year 1994 marked the establishment of the first scientific journal, the International Journal of Wireless Information Networks, and the first scientific magazine related to this subject, IEEE Personal Communications, which later changed its name to IEEE Wireless Communications. The pioneering textbooks in Wireless Information Networks [ 39 ] and Wireless Communications [ 40 ] also emerged in this era.

2.3 Evolution of WLAN Technologies and Standards After 1997

The IEEE 802 standards define MAC and PHY specifications of local networks as standards for vendors to be able to interoperate. Figure  3 shows the evolution of the PHY and MAC layers of the IEEE 802.11 standards from the beginning to the present. The first step of evolution of the standard after completion of the legacy 802.11 standard in 1997, was the IEEE 802.11b standard that used complex M-Ary orthogonal coding known as Complementary Code Keying (CCK). The IEEE 802.11b standard was completed in 1999. Devices using this standard operated at speeds up to 11 Mbps with a fall back to 1–2 Mbps using the legacy 802.11 standard. Both IEEE 802.11b and the legacy IEEE 802.11 devices operated in the 2.4 GHz bands. In 1999, the IEEE standards body also completed specifications for IEEE 802.11a operating at 5.2 GHz using OFDM transmission technology to achieve data rates up to 54 Mbps. The IEEE 802.11a PHY layer was coordinated with the efforts in the HIPERLAN-2 standard in Europe [ 41 ]. In comparison with Wi-Fi, the centralized MAC of HIPERLAN-2 [ 42 ] was expected to allow better management of quality of service, vital to the cellular telephone industry. Perhaps that was the motivation of Ericsson to pursue the leadership of this effort. However, in a manner similar to wireless ATM, this standard did not achieve commercial success. This could be because in wide area networking we have large number of users with less bandwidth resources and rationing this scarce resource requires centralized supervision by enforcing quality of service rules. In local areas with abundant availability of bandwidth and only a few users, a distributed MAC would be more practical in that time frame. Although a new standard for integration of local and metropolitan area networks, such as HIPERLAN-2 or wireless ATM, did not became a reality, the need for this integration of cellular networks with Wi-Fi, became a reality. The concept of integration of Wi-Fi with cellular using vertical hand-offs and mobile IP technology emerged in the early days of commercial popularity of Wi-Fi [ 43 ] and it has prevailed all the way along up to the time of this writing but this time with operating system and software control. The continuation of the ideal of new standards for local operation continued into Femtocells [ 44 ] and long-term evolution-unlicensed (LTE-U) [ 45 ] – operation of 4G cellular networks in the unlicensed spectrum, but neither has created any serious challenge to the Wi-Fi market yet. In the same way that the WLAN industry in its early days of survival resorted to point-to-multipoint outdoor installation for wider area coverage, it can be thought that Wi-Max [ 46 ] emerged as a successful application of local area centralized medium access control technologies for outdoor antenna deployments. The wireless ATM, HIPERLAN, Femto-cell, LTE-U and Wi-MAX technologies created a significant hype in scientific publication venues and among national funding agencies, but they failed to keep investors in developing these technologies as happy as those that invested in Wi-Fi. Later, in 2003, the IEEE 802.11g working group defined OFDM operation in 2.4 GHz with the same data rates as IEEE 802.11a, which expanded the horizon for Wi-Fi market.

figure 3

Evolution of Wi-Fi technologies and standards

The breakthrough in wireless communications at the turn of the twenty first century was the discovery of multiple antennae streaming benefitting from space time coding (STC) and MIMO technology. The foundation of multiple antenna streaming is based on two technologies: adaptive antenna arrays to focus the beam pattern of antennas and space time coding (STC) which is a coding technique enabling separation of multiple streams of data with coding. The benefits of multiple transmitting and receiving antennas existed in the antenna and propagation society literature since the 1930’s [ 47 ]. Seminal work on STC [ 48 , 49 , 50 ] enabled multiple streams of data and that is why it is considered as one of the most important worldwide innovations around the turn of the twenty first century. Multiple streams of data using MIMO technology in conjunction with OFDM and benefitting from STC opened a new horizon in scaling the physical layer transmission rates in multipath fading channels [ 51 ]. The next giant step in the evolution of technology for the IEEE 802.11 community was the introduction of IEEE 802.11n in 2009, using MIMO technology to enable multiple data streams to achieve raw data rates up to 600 Mbps both in the 2.4 and 5.2 GHz bands. Other standards such as IEEE 802.11ac and 802.11ax, followed the same OFDM/MIMO technology.

Another major hype in physical layer technologies for wireless communications was mmWave pulse transmission technology. The IEEE 802.11ad group adopted mmWave pulse transmission technology in the 60 GHz band with utra-wideband (UWB) transmission bandwidth exceeding 2 GHz to achieve data rates on the order of Gbps. Although mmWave technology became an important part of the 5G cellular networking industry [ 19 ], IEEE 802.11ad and 802.11ay, as the first completed standards using these technologies have not been successful in attracting a huge share of the WLAN market. As we explained earlier, mmWaves in indoor areas has coverage restriction that does not apply to outdoor antenna deployments.

Regarding the MAC of the IEEE 802.11, the main two techniques which became dominant were CSMA/CA and request/clear to send - RTS/CLS. Carrier sensing with collision avoidance—CSMA/CA was a practical extension to the wireless medium of CSMA/CD (collision detection), which was adopted for the IEEE 802.3 standard, commercially known as Ethernet. The IEEE 802.11 devices grew with the name “wireless Ethernet” and CSMA/CA would enable Ethernet to finally have a wireless extension. The RTS/CTS mechanism was originally designed to address the hidden terminal problem, but it became more popular for application with directional antennas for IEEE 802.11ac, ad. An analytical comparison of these two MAC techniques is a challenging problem that received a very thorough and popular analysis in the year 2000 [ 52 ].

A good survey of all these standards is presented at Wikipedia [ 53 ]. Here, we argue that all major PHY layer technologies evolved for wireless information networks: optical wireless, spread spectrum, M-ary orthogonal coding, OFDM, MIMO, and mmWave technologies were first adopted by the IEEE 802.11 standardization community. Then, DSSS and orthogonal signaling in 2G/3G, OFDM/MIMO in 4G, and mmWave in 5G/6G cellular telephone technologies, came after the adoption of these technologies in IEEE802.11 standards. The MAC of cellular telephone industry is centralized and different from that of WLANs, primarily to accommodate high traffic densities and support higher level of mobility for users with metropolitain area coverage. The IEEE 802.15 wireless personal area networks followed a similar pattern by adoption of FHSS for Bluetooth and DSSS for ZigBee, after they were first introduced by the IEEE 802.11 standard. The MAC of Bluetooth and in particular ZigBee carry similarities with those of the MAC of IEEE 802.11. Therefore, it is fair to say that the WLAN industry pioneered the design of the dominant PHY technologies of today’s wireless networking industry and this is a huge technological impact in the communication of humans, devices, and machines.

2.4 Evolution of Wi-Fi Applications and Market

Applications fuel the market, and they are intimately linked to the network through devices running these applications. Local area networks were networking computers to share common peripheral devices such as printers or storage memories and later machines in a manufacturing floor. In the late 1980’s and early 1990’s, when the WLAN industry began to test the market, Personal Computers (PC) and Workstations were competing to capture the market of mini-computers. Laptops became popular in this market a little later. From the networking point of view in that era, engineers were searching for wiring solutions for the growing market of these devices to connect them with minimal effort. The early WLAN startups were thinking of wireless as a replacement to wired LANs to connect PC’s in open areas such as manufacturing floors or open offices without partitions. These companies were assuming that these small computers will grow on office desks or on manufacturing areas in clusters. The idea was that if we connect this cluster of desktop computers to a hub and then we connect the hub to a central node connected to the Ethernet backbone, we will avoid snaking of wires or wires hanging from the ceilings of manufacturing floors and offices. In a typical startup proposal for venture capital, these companies were arguing that close to half of the cost of the LAN industry was associated with installation and maintenance of these networks, which can be vanishingly small when we use wireless technology. As a result, the first WLAN products were shoe box sized hubs and central units and following the above argument, these companies were estimating that a market of a few billion-dollars would emerge for these devices in early 1990’s. Based on this idea a typical startup company or a small group in a large company could raise up to $20 M at that time, adequate to support a design and marketing team to get the product going. Therefore, the early products from NCR, Proxim, Aironet, WINDATA, Motorola, NCR, Persoft, Photnics and others appeared in the market (see Fig.  4 a for samples of these products). The reader can find a variety of photos of these historical WLAN products in the proceedings of the first IEEE Workshop on WLAN [ 34 ]. This workshop was held in Worcester, MA, in parallel to the IEEE 802.11 official meeting to decide on the future of this industry. Around the year 1993 these products were in the market but the expected few billion-dollar market developed only to a few hundred million dollars. These sales were mostly for selected vertical applications and by research laboratories discovering the technology, not for the horizontal market for connecting desktop computers everywhere. This resulted in a retreat in the original few tens of companies, searching for a new market domain.

figure 4

a Some historical pioneering shoe box size WLANS designed by Motorola, Persoft, Aironet, and WINDATA, b the wireless PC cards and its access points in Roamabout designed by Digital Equipment Corporation

During the market crash of 1993 for the WLAN industry designed for connecting clusters of desktop computers, two new applications emerged. The first solution was point-to-point or point-to-multipoint WLAN bridges. The idea was to allow WLANs to operate outdoors and add a strong roof top antenna to take advantage of free space propagation and antenna gains to extend the expected 100 m indoor coverage to outdoor coverage spanning a few miles. As examples of these markets, two hospitals in Worcester, MA, which were a few miles away could connect their networks with low cost private WLANs, instead of using expensive leased lines from telephone companies. Or, Worcester Polytechnic Institute could connect the dormitories to the local area network of the main campus. The other idea was to design smaller wireless PC Cards for the emerging laptop market. Figure  4 b shows the picture of the Roamabout access point box and the laptop wireless PC Cards of the first successful product of that type, designed at Digital Equipment Corporation, Maynard, MA. These devices were the showcase of the second IEEE Workshop on WLANs, October 1996 [ 54 ]. Examples of a practical market for laptop operation included large financing corporations such as Fidelity in Boston, who would purchase laptops for their marketing, sales, and other staff. Such companies wanted their staff to be connected to the corporate network, when in office.

The next wave of market demand for WLANs was for small office/ home office (SOHO) application, which began around 2000. The authors believe that this story began in the mid-1990 with the penetration of the Internet to homes with service providers like America online (AOL) for small indoor area distribution of signals. The penetration of the Internet in homes fueled the development of cable modems and digital subscriber line (DSL) modems for high data rate home services and with that came the growth in the number of home devices and demand for Home-LAN technology. Several ideas such as using home wiring or electricity wiring for implementation of Home-LANs were studied, but Wi-Fi emerged as the natural solution. At that time, the price of a Wi-Fi access point (AP), such as the one made by Linksys, had fallen to below $100 and wireless PC Cards could be purchased with a reasonable price of a few tens of dollars. The original early shoe boxes had been selling for a few hundred dollars for the hub and up to a few thousand dollars for the AP! With these lower prices, coffeeshops and other small businesses could afford to provide free Wi-Fi and homeowners could bring Wi-Fi home. This was perhaps the first large market bringing Wi-Fi from office to the home. During the 2000’s despite the crash of the .com industry, the Wi-Fi market in this domain began to grow exponentially. The exponential growth of Wi-Fi for SOHO encouraged consumer product manufacturing to consider Wi-Fi for integration in their products (e.g., in digital cameras and TV monitors). This market was however not that large, and the ease of Wi-Fi networking did not exist. The integration of Wi-Fi in the iPhone was the next major marketing break-through for Wi-Fi popularity and market growth. Integration of Wi-Fi into smartphone increased the sale of Wi-Fi chipsets to billions and further enabled Wi-Fi based positioning. More recently Wi-Fi applications expanded by emergence of motion and gesture detection as well as authentication and security with Wi-Fi signals to facilitate human computer interaction. Figure  5 summarizes the evolution of Wi-Fi applications. We provide an overview of these cyberspace application using the Wi-Fi signal in the remainder of this paper.

figure 5

Evolution of Wi-Fi applications and market

3 From Wi-Fi to Wi-Fi Positioning – Emergence of Another “Killer App”

In the early 1990’s, when the expected market for WLANs did not emerge, those invested in this emerging technology began to discover reasons for the lack of success. These were the CEO’s of startups and managers of the WLAN projects in larger companies. Some were associating the lack of success to the delay in finalizing the IEEE 802.11 standard and some to the lack of a “Killer App”. The standard itself was completed in 1997 (with interoperability tests) and soon, deployment in SOHO scenarios appeared as the “Killer App” in the late 1990’s. Adoption of Wi-Fi in smart phones in the late 2000 s was another breakthrough “Killer App” of Wi-Fi technology, which enabled these devices to execute a number of user applications such as integration of search engines, email, and large file transfers using smart phones. Other networked applications that were typically done on a desktop using the Internet followed (e.g., e-commerce and banking). However, the Wi-Fi Positioning engine was perhaps the most innovative “Killer App” related to Wi-Fi technology that was introduced by the iPhone. When Steve Jobs introduced Skyhook of Boston’s Wi-Fi positioning technology in the iPhone, and he called it “Cool” and a “neat idea” [ 55 ], because it was different.

3.1 The Origin of Wi-Fi Positioning

Because of the commercial success of the Global Positioning System (GPS) in the mid-1990’s, the fact that GPS does not work properly in indoor areas, and the FCC mandate on E911 services for cellular networks, the indoor geolocation science and technology began to emerge in the late 1990’s [ 56 , 57 ]. The expensive cost of dense infrastructure needed for commercial positioning applications led that industry to resort to opportunistic positioning. Opportunistic Wi-Fi positioning using received Wi-Fi signals radiated from the Wi-Fi access points, originally deployed for wireless communications in office areas, was the first idea to attract attention for a cost effective indoor geolocation system [ 58 , 59 ]. The received signal strength (RSS) or time of arrival (TOA) of the signals radiated from the access points could be used for positioning since the locations of the access points were known and could be used for this purpose. The RSS was a quantity that was easier to measure but it was not accurate enough for good location granularity. The use of RSS for positioning became practical only by incorporating intelligence through training the system with fingerprinting and using pattern recognition algorithms to find the location [ 60 , 61 ]. This training was done with similar devices that collected data at known locations to make up the training fingerprints. The more accurate TOA measurements [ 59 ] needed additional design to be incorporated through a TOA acquisition system. The widespread deployment of Wi-Fi in office areas was more fertile for commercial development and a few companies, such as Ekahau, Helsinki, Finland, adopted that technology as their Wi-Fi positioning indoor geolocation system. Today this Wi-Fi positioning industry is sometime referred to as “real time positioning system” RTLS) [ 62 ]. The commercial success of RTLS was rather limited and it never generated a substantial market for Wi-Fi positioning.

Another approach to Wi-Fi positioning was to collect the fingerprint of locations from the APs from a vehicle driving in the streets and tagging the fingerprints with the GPS location reports of the vehicle at the time of measurement. This was the approach used by Skyhook Wireless, Boston, MA, which was adopted by the iPhone and was trademarked as “Wi-Fi Positioning System” (WPS) by Skyhook [ 62 ]. The difference between RTLS and WPS are: (1) RTLS typically covers only one building while WPS covers a metropolitan area, (2) fingerprinting in RTLS for a given area of coverage is much more expensive than WPS, (3) RTLS provides an accuracy of around a few meters while WPS provides for accuracies on the order of 10-15 m. Larger coverage areas with accuracies of 10-15 m enabled turn-by-turn direction finding for vehicles in the metropolitan areas that was the highlight of positioning applications in the original iPhone [ 55 ]. As a result, WPS became a commercial success and a highlight of the magic of iPhone applications. Today, Skyhook’s database contains over a billion AP locations worldwide and its database receives over a billion hits per day from smart device applications using WPS technologies. Google, Apple and other cyberspace giants have formed their own WPS system with their own database of APs along with that of the Skyhook.

3.2 Emergence of Location Intelligence from WPS

WPS is a device-based positioning system, i.e., a device reads the RSS of surrounding Wi-Fi devices with their MAC addresses. These readings are transferred to a central server with a database of the mapping of fingerprints to position and the system can determine the location of the device using the fingerprint database. Thus, communication link to carry these information between the device and the server is essential here, which Wi-Fi already provides. This process traditionally takes place in two steps, fingerprinting and positioning. During the fingerprinting phase, a data acquisition device located in a vehicle drives on the streets. In this phase the MAC addresses of the APs and their associated RSS are sampled approximately every second and each sample is tagged with the GPS reading of the location at the time of sampling. The fingerprint in the database, the MAC addresses and the GPS readings are post processed with proprietary algorithms to associate the MAC addresses of each AP to a location based on GPS readings. In this way, the WPS system builds a database of locations of the APs in the areas that the vehicles drive through. When a device, not knowing its location, sends the MAC address of an AP and RSS readings to the server, the server uses another proprietary algorithm to position the device and determine its location. The major WPS service providers, Skyhook, Google, and Apple, each receive approximately one billion requests per day from millions of devices. The one billion hits each associate a personal device address to a location and one can track the movements of the device. Applications drawing from this motion tracking capability of WPS are referred to as “location intelligent” and are said to be providing location-based services. One simple location intelligence application is the location-time traffic analysis. We may grade the density of hits per-hour of the day to determine where the people are going, and smart marketing strategies can benefit from that data. Other location intelligent applications include “geofencing” of elderly people, animals, prisoners, suspicious people, real world consumer behavior, location certification for security, positioning IP addresses, and customizing contents and experiences. During the recent COVID pandemic, Apple made its mobility data (when people were asking directions – and thus location information) public to enable assessing the social distancing and quarantine postures in various cities and communities [ 63 ]. Of course, this data also includes cell phone technology, but it is an indication of how far sensing signals from mobile devices has come, starting with Wi-Fi positioning.

The future directions in Wi-Fi positioning is in the integration of RSS signals with other sensor readings on smart devices (accelerometers for instance) to enhance the precision and flexibility of positioning. There are research works on integration of mechanical sensors such as accelerometer and gyroscope on robotic platforms with Wi-Fi positioning [ 64 ], there are works in integrating more precise UWB positioning with limited coverage with wider coverage Wi-Fi positioning [ 65 ], and frameworks for generalizing fingerprinting in multi-sensor environment [ 66 , 67 ]. Other researchers investigate submeter Wi-Fi positioning using Wi-Fi channel state information (CSI) [ 68 ].

4 Wi-Fi and Emerging Cyberspace Applications

Wi-Fi localization, either for local indoor areas (what we referred to above as RTLS) or for metropolitan areas (which we referred to as WPS) makes use of the RSS feature from APs that are broadcasting signals, by reading their broadcast quasi-periodic beacon signal. Beacons are used to advertise the availability of an AP thereby enabling other devices like smartphones or laptops to access the Wi-Fi network (called basic or extended service area in the standard). A device that reads the beacon only for localization does not need to connect to the AP because it only needs the MAC address and the RSS information for positioning itself. Access Points radiate a radio frequency (RF) cloud around themselves which are available to any device in their area of coverage. The RSS is one feature of the Wi-Fi RF cloud, which can be measured easily without any coordination between the transmitter and the receiver. With some coordination, the receiver can measure the Time of Arrival (TOA) of a signal as well. Today, the dominant transmission technique in Wi-Fi is MIMO-OFDM. Devices which can use MIMO_OFDM can also measure Direction of Arrival (DOA), channel impulse response (CIR), and the Channel State Information (CSI) of the multipath medium between the transmitter and the received [ 69 ]. These features (as well as the RSS feature) vary statistically depending on the multipath characteristics arising from the motion in the environment. Consequently, it is possible to use these features for motion and gesture detection. In this section, we begin by describing these statistical behaviors and following that we briefly review the emerging research benefitting from analyzing these statistical changes in features, toward the design of cyberspace applications for human-computer interactions.

4.1 Characteristics of Features of Wi-Fi Signals in Multipath

Figure  6 , illustrates a general line-of-sight (LOS) scenario for a MIMO-OFDM Wi-Fi communication with typical multipath propagation. Multiple paths are reflected from walls and other stationary and moving objects in the environment. These paths are often clustered due to scattering from smaller objects located close to each other. In an ideal situation, the stationary baseband CIR for wireless devices operating in multipath indoor areas is represented by:

where \((\alpha_{i} ;\tau_{i} ;\theta_{i} ;\psi_{i} )\,\) are the magnitude, TOA, phase, and DOA of the i-th path, and N is the number of multipath components. In this equation the phase of the arriving path and the TOA are related by \(\theta_{i} = 2\pi f_{c} \tau_{i} \,\) . Therefore, if we measure the TOA, we can calculate phase and vice versa. Since the phase is a periodic function, in calculation of the TOA from the phase we should consider such ambiguities [ 57 ]. The TOA, amplitude, and phase of each path as well as the RSS can be calculated from the length of the path, by:

, where \(f_{c} \,\) is the carrier frequency of the signal, \(\lambda = c/f_{c} \,\) is the wavelength of the signal, \(d_{i}\) , is the length of the path, and c is the speed of light. If we have an antenna array, we can calculate the DOA from TOA differences between the received signals from different array elements. When we have motion in the environment, either by moving the location of the devices or objects move in the environment, the lengths of the various paths change affecting features such as RSS, TOA, and DOA. In addition, due to Doppler shift effects, a change in the length of a path with the velocity of \(v_{i} \,\) meters per second causes a frequency off-set in the carrier frequency calculated from [ 57 ] :

figure 6

Multipath scenario of RF propagation for Wi-Fi enabled indoor wireless communications using OFDM/MIMO technology

In summary, if a receiver can measure the CIR and the frequency off-set, it can monitor the length and direction of the path as well as the velocity of changes in the path lengths.

As shown in Eq. ( 2 ), the amplitude of the received signal, \(\alpha_{i}\) , from a path changes inversely with the increase in the path length, \(d_{i}\) . The phase of the arriving signal from a path, \(\theta_{i}\) , changes rapidly for a value of \(2\pi\) each time the length of the path increases by a value equivalent to the wavelength of the signal, \(\lambda\) . The rapid change in phase of the arriving paths causes fading and these rapid changes are caused by motion of the device, motion of people around the devices, and by the changes in frequency of operation. In the wireless communication literature, fading characteristics is studied under temporal, frequency-selective, and spatial fading [ 39 ].

The traditional application of measurements of the CIR is in high-speed wireless communications and in radars. Modern applications that use CIR measurements are in wireless positioning, gesture, and motion detection, and in authentication and security. Each application relies on certain specific features of the CIR and for that needs to measure those features with certain precision at the receiver. The accuracy of measurement of these features at a receiver relies on training (known signals), the bandwidth of the system, availability of antenna arrays at the receiver, and accuracy of synchronization between the transmitter and the receiver. As a result, the specific implementation of these applications have unique challenges and demand research and development and decades of years of evolution. Traditional radar and digital communication systems were built around the second World War and today we are still developing new cyberspace applications around them. What is changing is the application environment and characteristics of multipath inside those environments. As we move from open areas to sub-urban, densely populated urban areas, and indoor areas, the multipath propagation of RF signals increases, and design of applications faces new challenges.

The measurement of multipath characteristics of the channel was a very challenging problem in the early 1970’s [ 70 ]. Wideband digital communication systems evolving in this era needed the estimate of multipath arrivals to enhance their data rates. Wideband multipath channel measurement in that period would be a subject for a Ph.D. thesis [ 71 ]. Today, all wireless communication devices measure the multipath characteristics as a routine in the design of their systems.

If the bandwidth of the system is wide enough so that the width of the transmitted communication symbols, the inverse of the bandwidth, is less than the inter-arrival time of the paths, a sensitive enough receiver can isolate each path and measure the features precisely. If the bandwidth of the channel is not wide enough, a receiver can only detect a cluster of paths as one path. In wireless communications we can categorize device receivers into three categories, ultra-wideband (UWB), Footnote 3 wideband (WB), and narrowband (NB). UWB systems are capable of measuring most individual paths, WB receivers measure multipath arrivals but each path is in reality an aggregate of a cluster of paths, and NB receivers receive the signal from many paths as essentially a single path that combines all multipath arrivals (see Fig.  7 ). When a receiver detects a path that is indeed the combination of several neighboring paths, due to fast variations of the phases of the original path, the amplitude of the detected path experiences Rayleigh or Rician fading and the TOA of the detected path obviously is something very different from any of the individual paths in wireless communication applications, fading causes huge degradation of the maximum achievable data rate, and to compensate for that the research community have discovered equalization, spread spectrum, OFDM, and MIMO technologies in the past several decades [ 39 ]. The popular TOA-based location related applications measure the distance from the delay of the TOA of the direct path between the transmitters and the receiver and integration of multiple paths in a single path at the receiver causes huge errors in distance estimation (1 m error for every 3 ns error in delay).

figure 7

Multipath detection in UWB and multipath clustering in WB and NB receivers

The receivers of wireless communication devices employing these technologies measure the characteristic of the communication channel and characteristics of these measurements vary, depending on the architecture and bandwidth of the system. Empirical measurements and modeling of multipath RF propagation in indoor areas in the late 1980’s, first showed that if the bandwidth exceeded 100 MHz the amplitude of multipath arrivals follows a lognormal distribution, caused by shadowing, and they do not follow the commonly assumed Rayleigh or Rician multipath fading characteristics [ 24 ]. Therefore, we may consider Wi-Fi technologies using bandwidths on that order as UWB systems that can resolve the paths. The characteristics of CIR measurements with UWB systems is that the amplitude of the paths follow a lognormal distribution that is much more stable than Rayleigh/Rician distributions, and the TOA measurements are precise for calculation of the delay of the paths. The IEEE 802.11ad devices certainly follow the UWB characteristics. The IEEE 802.11ac options with bandwidth up to 160 MHz gets close to observing UWB features. However, legacy IEEE 802.11 and the popular 802.11 a,g,n,ac,ax,af can be considered as WB systems with bandwidths of approximately 20-40 MHz (and sometimes up to 80 MHz). The IEEE 802.11 standards using OFDM have sub-carriers with a bandwidth of approximately 20 MHz/64 = 375 kHz per carrier, which is considered NB. In summary, channel measurements for NB transmissions provide for a stream of Rayleigh fading amplitudes and uniformly distributed phases. The phase measurements do not support a reliable measure of distance and WB systems provide multiple streams of NB data. The UWB systems provide for multiple streams of slow lognormally fading signals with multiple streams of phases that are beneficial for accurate measurements of the delays of the associated paths.

The most popular Wi-Fi devices at the time of this writing, IEEE802.11n and IEEE 802.11ac, use MIMO-OFDM technology with three transmitters and two receiver antennas, shown in Fig.  6 . The OFDM signal has 64 sub-carriers, using 52 of these carriers for communication data. In addition, to the magnitude and phase of the carriers they also provide the frequency off-set from the center frequency as well as six streams of magnitude and phases of the CSI data. Depending on the quality of the beam forming algorithm to sharpen the beam, the CSI data can represent a single path or a cluster of paths arriving from a direction. If it is a cluster, the amplitude samples have a Rayleigh distribution and if it is a single path the amplitudes should be more stable with a lognormal fading behavior. The number of paths in the cluster also governs the accuracy of delay of the path measurement using the phase of the received CSI stream [ 72 ]. Recently, these data streams have been paired with artificial intelligence (AI) algorithms to initiate research in several cyberspace applications.

4.2 Emerging Cyberspace Applications of Wi-Fi

In recent years, researchers have studied a variety of Wi-Fi “RF cloud” features in several cyberspace applications and for the enhancement of local area positioning systems. The idea of using the preamble of OFDM signals, first introduced in HIPERLAN-2/IEEE 802.11a standard, for TOA localization was discussed at the emergence of this standard in [ 59 ]. In this work, the pseudo noise (PN) sequence used in the preamble of the OFDM signal is used for TOA positioning. Like the measurement of timing in GPS, the TOA is measured from the time displacements in the sharp peak of the autocorrelation function of the PN-sequence. It is also possible to measure the TOA from the phase of the received signal; however, this is very sensitive to multipath fading [ 57 ]. OFDM/MIMO systems reduce the multipath allowing a more accurate measurement of TOA using the phase of the received signal. Recently, the measurement of TOA using the phase of Wi-Fi signals with OFDM/MIMO was used for fine-grained micro-robot tracking in [ 68 , 73 ]. The experience in that work suggests that in a line-of-sight (LOS) situation (close distance between transmitter and receiver), where the multipath features are not significant, the phase of the received signal provides a reasonable estimate of the distance. Others have experimented with CSI fingerprints to enhance indoor Wi-Fi positioning [ 74 ]. As we explained in Sect.  4.1 , CSI provides multiple streams of magnitude and phase and the phase information can be used for TOA estimations. The research trend in [ 59 , 68 , 73 , 74 ] opens a horizon for higher precision Wi-Fi positioning, as they are compared to RSS based Wi-Fi positioning in local indoor [ 58 , 75 ], and wider metropolitan areas [ 62 , 76 , 77 ].

In the past decade, the design of novel “cyberspace intelligence” applications that opportunistically benefit from the “RF cloud” radiated from signals used for wireless communications and short range radars, has been a fertile area of research [ 69 ]. These applications take advantage of statistical variations of RF signals propagated from the wireless devices, caused by motion in the environment to design applications that can detect gestures and motion or those used for authentication and security. Because of the widespread deployment and reach of Wi-Fi access points and the availability of Wi-Fi chip sets in almost every personal electronic device, as described in Sects.  2.4 and 2.5, a large body of this literature has evolved around this Wi-Fi RF cloud. Once again, the simplest feature of the Wi-Fi RF cloud is the RSS. Motions in the environment cause multipath fading resulting in changes in the RSS and statistics of this fading behavior as it relates to the speed of motion. The applications benefit from this behavioral change in the RSS to develop simple possibilities in detecting motion related human activity. This trend of research began in the early 2010’s and has evolved throughout that decade. As an example, the time- and frequency-domain multipath fading characteristics of the RSS from body mounted health monitoring sensors was examined in the lead author’s laboratory in the early 2010’s to differentiate among standing, walking, and jogging activities by humans [ 78 ]. In the mid-2010’s, when the infusion of AI to applications became popular, the same idea with more complex activity classifications with AI algorithms was pursued [ 79 ]. In the middle of these activities, hand motion classification using RSS and the frequency offset of OFDM signals from Wi-Fi devices when motion occurs between two devices without any body mounted device was reported to differentiate nine hand gestures [ 80 ]. Research in that direction encouraged the consideration of a more advanced feature such as the CSI for similar applications and suddenly a large body of literature emerged for a variety of related applications for human computer interaction that made extensive use of the statistical behavior of CSI from Wi-Fi signals. Approximately 150 of these papers are classified in [ 81 ]. These papers use CSI toward what is called as “device-free” human activity detection [ 82 ] all the way up to micro-gesture detection applications such as detecting hand motion while typing [ 83 , 84 ].

In gesture and motion detection using RF signals, the work takes advantage of the effects of motion on changing the multipath propagation of signals and the resulting change in statistical behavior of the features of RF cloud to classify human activity. Similarly, it is possible to use the uniqueness of these variations for individual human motions to identify a person. For example, when we train a computer to detect the keystroke of a person using the CSI from Wi-Fi signals, the same system can identify that person as the keystrokes of one individual vary from that of another. This way, using the CSI for keystroke detection can also be used for human authentication for security purposes [ 83 ]. In recent years, a body of literature has also evolved for applications of the Wi-Fi RF cloud in authentication and security. Again, the simplest feature that is used is the RSS and it is possible to use the RSS behavior of body mounted sensors to identify a person [ 85 ]. Fundamentally, authentication is a binary decision-making process (is it Alice or not?) and activity classification is a multiple classification problem (is Alice jogging, walking, standing, or sitting?). In authentication we compare RF feature characteristics of one person to others, while in activity classification we usually compare different activities of a single person. Such similarities have led to the emergence of literature in using more complex CSI from Wi-Fi signals for device-free authentication such as those in [ 86 ]. Another survey of these categories of applications is available in [ 87 ].

The problem of entity authentication is for security – whether an authorized individual is performing an action. However, there is another branch of security application, concerned with generation of unique (and random so that it cannot be guessed) keys for encryption of the data communication between wireless devices sharing this natural broadcasting medium. The fundamental idea comes from the fact that the wireless communication channel between two devices is reciprocal [ 88 , 89 ]. Therefore, when we measure the features of the communication channel between two devices, these features should be the same. However, the details of the electronic implementation of a device is unique to itself and that results in measurements which are not identical. If we can model these differences by a measurement noise, then we can quantize the measured feature based on the measurement noise to establish the same key at two ends of a wireless communication link. A survey of these physical layer security systems is available in [ 90 , 91 ]. Geo-fencing, to ensure that Wi-Fi signal propagation can be confined to the inside of a building is another interesting application of radio propagation for information security [ 92 ].

Although in past decade these cyberspace applications of Wi-Fi signals have attracted significant intellectual attention for research, the commercial market is still waiting for a “Killer App” like Wi-Fi positioning and tracking. The industry is waiting for the next popular application of Wi-Fi signals to enhance cyberspace intelligence further.

5 Conclusions

In this paper we presented a historical perspective of the evolution of Wi-Fi technology in the way that the principal author experienced it (and subsequently the second author) since the inception of this industry in early 1980’s. The paper was prepared as a part of a special issue on the 25 anniversaries of the International Journal of Wireless Information Networks, which was established in 1994 as the first journal fully devoted to wireless networks. In the paper, we began by describing how Wi-Fi has impacted our daily lives and why it is playing this important role. Then we discussed how the dominant physical layer wireless communication technologies, wireless optical, spread spectrum, OFDM and MIMO, and mmWave UWB technologies, were first implemented in the IEEE 802.11 standards for Wi-Fi and how indoor radio propagation studies were conducted to enable these technologies. The rest of the paper illustrated how the RF cloud propagated from Wi-Fi devices enabled important cyberspace applications. We began this part by describing how Wi-Fi positioning revolutionized indoor geolocation science and technology. Then we explained how the RF cloud of Wi-Fi devices has enabled diverse cyberspace applications such as motion and gesture detection as well as authentication and security to hopefully lead the way to another revolution in human computer interfacing.

A version of this paper was originally presented as a keynote speech entitled “Evolution of Wi-Fi Access and Localization – A Historical Perspective”, IEEE VTC, Boston, MA, May 6, 2015. Material presented further evolved in other keynote speeches, the last one in Cybercon’19, Beijing, China, December 16, 2019.

In the recent pandemic, parents and children are using Wi-Fi for work and school, and its untethered feature has made a big difference to the way people have coped with social distancing and quarantines.

We use the term UWB differently here than before, where extremely narrow pulses of bandwidth on the order of a GHz are used for fine grained localization. The term UWB is also now used by commercial 5G systems differently.

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Pahlavan, K., Krishnamurthy, P. Evolution and Impact of Wi-Fi Technology and Applications: A Historical Perspective. Int J Wireless Inf Networks 28 , 3–19 (2021). https://doi.org/10.1007/s10776-020-00501-8

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Portable and wearable self-powered systems based on emerging energy harvesting technology

  • Chen Xu 1 ,
  • Yu Song   ORCID: orcid.org/0000-0002-4185-2256 2 ,
  • Mengdi Han 3 &
  • Haixia Zhang 1 , 2  

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A self-powered system based on energy harvesting technology can be a potential candidate for solving the problem of supplying power to electronic devices. In this review, we focus on portable and wearable self-powered systems, starting with typical energy harvesting technology, and introduce portable and wearable self-powered systems with sensing functions. In addition, we demonstrate the potential of self-powered systems in actuation functions and the development of self-powered systems toward intelligent functions under the support of information processing and artificial intelligence technologies.

Introduction

In recent years, portable and wearable electronic devices have been in a stage of rapid development 1 , 2 . Personalized electronic devices such as smart watches and smart glasses have sprung up, bringing much convenience to people’s life 3 , 4 . At the same time, with the promotion of flexible electronic technology 5 , big data technology 6 , 7 and artificial intelligence technology 8 , portable and wearable electronic devices have shown the development trend of flexibility, integration, and intellectualization, which have also facilitated rich applications such as health monitoring 9 , 10 , human–machine interaction 11 , 12 , and the Internet of Things 13 , 14 .

For portable and wearable electronic devices, the energy supply is a major obstacle to its flexible and integrated application. Replaceable batteries are now the common energy source of electronic devices. However, the rigid characteristics of these batteries limit the overall flexibility of electronic devices. The limited life of batteries and potential environmental pollution problems also do not conform to the principles of sustainable development. As a result, many efforts have been made to explore new environmentally friendly, renewable energy sources to power electronic devices.

Self-powered technology provides a solution for the sustainable energy supply of portable and wearable systems. Self-powered technology means that the device can maintain its own operation by collecting energy in the working environment without an external energy supply. The effective collection of various forms of energy in the working environment is the basis of self-powered technology.

The energy sources available for portable and wearable electronic devices, such as mechanical energy, thermal energy, chemical energy, and solar energy, are extensive. According to the characteristics of these forms of energy, energy harvesting systems suitable for collecting various forms of energy have gained substantial attention. For example, triboelectricity is generated during the contact-separation process of two different materials. Piezoelectricity is produced during the mechanical deformation of piezoelectric materials, and electromagnetic power comes from the conductor moving through a stationary magnetic field. Conversion from mechanical energy to electrical energy exists in all the above transduction mechanisms. Therefore, triboelectric 15 , 16 , piezoelectric 17 , 18 , and electromagnetic 19 , 20 energy harvesters are suitable for harvesting mechanical energy during human motion. Biofuel cells use enzymes or microbes as catalysts to convert chemical energy into electrical energy. The large amount of chemical energy contained in the biochemical environment of the human body can act as an ideal energy source for biofuel cells, making it a potential method for harvesting human chemical energy 21 , 22 . The human body temperature is constant, and there is a temperature difference with the outside world. Therefore, thermoelectric generators can convert the heat of the human body into usable electric energy as a stable source of energy 23 , 24 . Solar energy is also a kind of green renewable clean energy that is an ideal power source for wearable electronic devices 25 , 26 . Furthermore, hybrid energy harvesters that integrate capabilities of harvesting various forms of energy further improve the efficiency of energy harvesting and broaden the application scenarios 27 , 28 .

Sensing technology plays a decisive role in our life as an important way for electronic devices to obtain external information. With the development of self-powered technology, self-powered sensing systems that can sense important physical 29 , 30 , chemical 31 , 32 , and biochemical 33 , 34 information without an external energy supply have gradually come into people’s view. Since there are no issues such as battery replacement and environmental pollution, a self-powered sensing system is expected to be the major form of sensor nodes in the future Internet of Things era. There are two main ways to realize self-powered sensing. Active sensing can be realized by using the output electrical signal itself as the sensing signal. For example, a triboelectric signal can be used as a sensing signal to realize pressure sensing 35 . Using energy harvesting technology to provide energy to a sensor module is another way to realize self-powered sensing 36 . For instance, many self-powered systems based on piezoelectric 37 , thermoelectric 38 , wet electricity 39 , and redox electricity 40 have been studied to power sensor modules and realize active sensing.

Electronic devices such as actuators can assist humans in completing diverse and complex operations in specific scenarios. The development of self-powered technology makes it possible to realize various actuation functions without an external energy supply. For example, many researchers use electrical energy converted from other forms of energy as an excitation signal to realize the functions of automatic control 41 , 42 , microfluidics 43 , 44 , drug delivery and release 45 , and adjuvant therapy 46 , 47 .

From a long-term point of view, we will eventually witness human society entering the age of intelligence. The Internet of Things, artificial intelligence, and big data technology change our lives with each passing day. The relationship between human beings and electronic devices has also presented an unprecedented state. Electronic devices with a single function will no longer meet the functional requirements of portable electronic devices in the intelligent era. Portable and wearable self-powered intelligent systems are gradually replacing bulky computers as the interface of a new generation of intelligent human–machine interactions and playing an important role in intelligent identification 48 , intelligent control 49 , and other fields.

Self-powered systems are gradually becoming the mainstream trend in the development of electronic devices. Recently, some articles have summarized the latest development of self-powered systems. For example, Khalid et al. summarized the human-powered energy harvesting technology that can be used in smart electronic systems 50 . Dong et al. started with self-powered sensors and presented state-of-the-art works based on the synergy between wearable sensors and AI technology 51 . Wang et al. and Gunawardhana et al. focused on triboelectric energy harvesting technology and introduced in detail the development and application of wearable triboelectric nanogenerators (TENGs) in self-powered systems 52 , 53 . However, the integration of multiple energy harvesting methods and the diversification of system functions are inevitable development directions of self-powered systems in the future. Therefore, a comprehensive overview of multiple energy harvesting methods and the diverse applications of self-powered systems helps to further establish the connection between energy harvesting and self-powered applications. Starting from such a foothold, we covered a wide range of energy harvesters and diversified self-powered system functions in this review. We first focus on the development of self-powered systems based on emerging energy harvesting technology. In the second part, portable and wearable energy harvesting technology based on various principles is introduced. The third part presents some self-powered sensing systems based on various principles. The fourth part mainly introduces the representative self-powered actuation system. The fifth part mainly shows the self-powered intelligent system as the human–machine interaction interface, and the synergy between self-powered sensors and AI technology is presented. In the sixth part, we discuss our perspective of the development direction of this field.

Portable and wearable energy harvester

Energy harvesting is the basis of a self-powered system. Additionally, for consideration of convenience and environmental protection, we need sustainable, clean, and renewable energy to power portable and wearable devices. There are various forms of energy in the environment, including not only the energy produced by the human body itself but also the energy provided by the external environment. In daily life, human mechanical movements such as finger movement, walking, and running can produce considerable mechanical energy. However, due to the multimode and low-frequency characteristics of human mechanical movement, it is not easy to collect the mechanical energy of the human body effectively.

Triboelectric and piezoelectric generators are the two most common ways to collect mechanical energy generated by human motion. Triboelectric energy harvesting is based on the well-known principle of friction electrification. The contact of two different objects will induce static charges on the surface of the objects. Subsequently, the relative motion between the two charged objects will produce a potential difference, thus driving the flow of charges. Due to the advantages of a wide selection of materials, low operating frequency and high output power, TENGs have become the most common ways of collecting the mechanical energy of human motion.

In Fig. 1a , an arch-shaped TENG was proposed by Z. L. Wang’s research group in 2012 54 . The pyramid patterns on the surface of the TENG help increase the output of the TENG by increasing the contact area of the two triboelectric layers. The output voltage, current density, and energy volume density of the TENG reached 230 V, 15.5 μA cm −2 and 128 mW cm −3 , respectively. The energy conversion efficiency is as high as 10–39% and meets the demands of wireless sensor systems and mobile phones. This work demonstrates for the first time the potential of TENGs for driving personal mobile electronic devices and shows how TENGs can affect lifestyle.

figure 1

a Arch-shaped TENG as a power supply for mobile phones. Reprinted from ref. 54 with permission. b Hybrid nanocomposite generator (hNCG) for hand movement energy harvesting. Reprinted from ref. 68 with permission. c Flexible thermoelectric generator (f-TEG) for harvesting human thermal energy. Reprinted from ref. 73 with permission. d Wearable textile-based hybrid supercapacitor–biofuel cell (SC–BFC) system as a biochemical energy harvester. Reprinted from ref. 77 with permission.

Since invented by Wang in 2012, TENGs have been studied systematically in materials 55 , 56 , structure 57 , 58 , working mode 59 , 60 , 61 , 62 , and power management 63 , 64 , during which time, the output of TENGs has been greatly improved. As the most suitable energy harvesting method for human motion mechanical energy, TENGs still face some shortcomings that need to be overcome. For example, stability and reliability are the largest problems. The ability of reliable power management and tolerance to environmental factors such as humidity and temperature also need to be further studied.

As another common means of harvesting mechanical energy, piezoelectric generators have particularly attracted much attention because of their high energy density characteristics and clearer physical mechanism. Piezoelectric energy harvesting is based on the piezoelectric effect. Electrical energy can be generated by the mechanical deformation of piezoelectric materials. Common inorganic piezoelectric materials are hard materials 65 and are not suitable for portable and wearable devices. With the development of materials science, flexible polymer piezoelectric materials have entered people’s field of vision 66 , 67 . With excellent flexibility, polymer piezoelectric materials can conformally attach to the surface of the human body, greatly improving wearability and further promoting the development of piezoelectric energy harvesters for human body mechanical energy harvesting.

Jeong et al. developed a 1D–3D (1–3) fully piezoelectric nanocomposite using perovskite BaTiO3 (BT) nanowire-employed poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) for a high-performance hybrid nanocomposite generator (hNCG) device (Fig. 1b ). The output of the flexible hNCG reached 14 V and 4 μA when used for hand movement energy harvesting. Such output performance is higher than the current levels of even previous piezoceramic film-based flexible energy harvesters 68 .

In addition to collecting hand movement energy, piezoelectric energy harvesters also play important roles in intelligent shoes 69 , implantable devices 70 , and intelligent fabrics 71 for biomechanical energy harvesting. Compared with TENGs, piezoelectric energy harvesters may be limited by the range of material selection. However, the advantages of a higher output current, simple structure, and working mode still make piezoelectric generators an indispensable method for mechanical energy harvesting.

In addition to the mechanical energy produced by human motion, the heat energy of the human body is also valuable energy that can be collected and utilized. The body temperature is constant, and there is a temperature difference with the external environment. Therefore, the use of a thermoelectric generator can achieve continuous energy harvesting. The working principle of a thermoelectric generator is based on the Seebeck effect, that is, the diffusion of electrons and holes caused by a temperature gradient.

There have been many studies on flexible thermoelectric generators (f-TEG) using inorganic thin-film thermoelectric materials or organic compound materials 72 . However, the energy conversion efficiency of these thermoelectric generators is too low to meet the power demand of portable and wearable electronic devices.

To address this problem, Yuan et al. proposed a systematic optimization method for designing a f-TEG (Fig. 1c ) 73 . Through optimizing the number of thermoelectric grains, the fill factor and the series–parallel connection mode of the f-TEG, high energy efficiency and power matching with a wearable sensory system are achieved. Such a highly efficient f-TEG utilizing bismuth telluride grains assembled on a flexible polyimide substrate exhibits excellent power performance with power densities of 3.5 µW cm −2 and 12.3 µW g −1 and can drive the sensor module and finally form a self-powered wearable human body sensing system. Compared with mechanical energy harvesting technology, thermoelectric generators are passive energy harvesting technologies. A thermoelectric generator can achieve continuous and long-term energy collection without any movement of the human body because of the stable energy source. By further increasing the power output, thermoelectric generators may become an ideal energy supply method for portable and wearable self-powered devices in the future.

The human body is a complex physiological environment. In addition to biomechanical energy and heat energy, the human body also has available chemical energy. Biofuel cells are an energy harvesting technology that can collect chemical energy from the human body. Biofuel cells are mainly divided into enzyme-catalyzed biofuel cells and microbial cell catalytic fuel cells that use biofuels such as ethanol 74 or glucose 75 to realize the conversion of chemical energy to electric energy.

In the current research, the output power density of biofuel cells is approximately a few microwatts per square centimeter. This level of energy output makes the biofuel cell insufficient to supply sufficient energy in any actual scenario 76 .

Therefore, Lv et al. integrated biofuel cells into self-charging units and presented a wearable textile-based hybrid supercapacitor–biofuel cell (SC–BFC) system (Fig. 1d ) 77 . This kind of biofuel cell can scavenge biochemical energy from human sweat and store it in a supercapacitor module. A hybrid energy system integrated with an energy harvesting and energy storage module can solve the problem of the small output energy of biofuel cells and ensure a stable energy supply.

On the basis of single energy harvesting technology, a hybrid energy harvesting system integrated with multiple modes takes advantage of various energy harvesting methods and improves the energy efficiency. For example, biomechanical energy can only be collected when the human body maintains a specific motion posture, while the thermoelectric generator can continuously harvest energy regardless of the human state.

In Fig. 2a , Lee et al. fabricated a highly stretchable, hybrid energy-scavenging nanogenerator based on a micropatterned piezoelectric P(VDF-TrFE) polymer, a micropatterned polydimethylsiloxane (PDMS)–carbon nanotube composite and graphene nanosheets 78 . The P(VDF-TrFE) polymer has both a piezoelectric effect and excellent thermoelectric properties with pyroelectric coefficients up to ≈200 µCm −2  K −1 . This kind of hybrid energy harvester can realize the simultaneous collection of biomechanical energy and heat energy when attached to a human hand, a shoulder, an elbow, and other parts.

figure 2

a Piezoelectric and thermoelectric hybrid energy-scavenging nanogenerator. Reprinted from ref. 78 with permission. b Solar-triboelectric hybrid energy harvesting system. Reprinted from ref. 79 with permission. c Hybrid energy harvester based on triboelectric and electromagnetic principles. Reprinted from ref. 80 with permission.

As a kind of sustainable clean energy, solar energy plays an important role in the field of energy harvesting. The simultaneous collection of solar energy and biomechanical energy is also regarded as an effective means to improve energy collection efficiency. H. X. Zhang’s group proposed a solar-triboelectric hybrid energy harvesting system (Fig. 2b ) 79 . Through the design of a common electrode structure and the introduction of an energy management module, this kind of hybrid energy harvester achieves a better charging effect than a single energy harvesting mode in the charging test of capacitors.

In addition, this research group has also proposed a hybrid energy harvester based on triboelectric and electromagnetic principles (Fig. 2c ) 80 . Compared with TENGs, electromagnetic generators are different in principle, output characteristics, and applicable frequency. Therefore, integration can achieve complementary advantages to adapt to a variety of applications. In this work, a flexible hybrid energy harvester is proposed based on magnetic and conductive PDMS material, which complements the large output voltage characteristics of TENGs and the large output current characteristics of electromagnetic generators. This hybrid energy harvester can charge a capacitance of 10 μf to 3 V in 110 s, which is superior to the TENG or EMG only.

Energy harvesting technology is the basis of self-powered systems, giving these systems the ability to achieve some functions without an external energy supply. As a necessary way for electronic devices to perceive the external environment, sensors are the cornerstone of the rich functions of electronic devices. Sensors used in wearable scenes can also act as an extension of five human sense organs, giving humans stronger environmental perception capabilities. Therefore, self-powered systems have great application potential in the wearable sensing field.

Portable and wearable self-powered sensing system

On the basis of energy harvesting technology, a variety of portable, wearable self-powered sensors for monitoring physical, chemical, and physiological information have been developed. There are two main ways to realize self-powered sensing. The first is active sensing, which uses the electrical signal itself as the sensing signal, where the output electrical signal will be affected by some external factors. Active sensing has been widely used in the monitoring of pressure 81 , humidity 82 , and temperature 83 .

In Fig. 3a , Liu et al. reported self-powered epidermal electronics with a tactile sensing function 84 . This kind of epidermal electronics can reflect the pressure on the skin, as well as the pressure distribution, through the triboelectric signal, which is of broad potential interest in wearable electronics.

figure 3

a Self-powered epidermal electronics with the tactile sensing function. Reprinted from ref. 84 with permission. b Flexible wearable pressure sensor based on piezoelectric materials. Reprinted from ref. 85 with permission. c The new kind of self-powered strain sensor based on redox electricity. Reprinted from ref. 86 with permission. d Active humidity sensor based on a moisture-driven electrical generator. Reprinted from ref. 87 with permission. e Self-powered thermoelectric glucose sensing system integrating the wearable thermoelectric generator (WTEG). Reprinted from ref. 88 with permission. f Self-powered glucose sensing smart watch based on photovoltaic cells. Reprinted from ref. 89 with permission. g Flexible perspiration-powered electronic skin (PPES). Reprinted from ref. 90 with permission.

The output of the piezoelectric material will be affected by its deformation degree, which allows these materials to realize the active sensing of human body postures.

Yang et al. showed a flexible wearable pressure sensor based on piezoelectric materials (Fig. 3b ) 85 . On the basis of organic/inorganic piezoelectric material BaTiO3(BTO)/polyvinylidene fluoride(PVDF) composites, polydopamine was introduced as a surface modifier to modify BTO, bringing the improved dispersion of BTO in the organic PVDF matrix. The pressure sensor can realize the active sensing of the elbow bending posture when placed on the elbow of the human body.

In addition, a new self-powered strain sensor based on redox electricity has been proposed using a graphene/Ecoflex film and meandering zinc wire (Fig. 3c ) 86 . In the state of stretching, the resistance of graphene/Ecoflex increases with increasing strain, which leads to a decrease in the redox current. This sensor has been utilized to realize the active motion detection of knee joints.

However, pressure sensors based on triboelectric or posture sensors based on piezoelectric and redox electricity are only small-scale laboratory devices as the primary prototype. To achieve a stable wearable active sensor that can cover the whole body of the human body, it is also necessary to solve key problems such as weak device stability and a single sensing mode.

In Fig. 3d , Shen et al. developed an active humidity sensor based on a moisture-driven electrical generator, which realizes the sensing of another physical quantity in addition to the common pressure and strain 87 . The output voltage of the generator is strongly dependent on the humidity of the ambient environment. This new type of device can play the role of self-powered wearable human breathing monitors and touch pads.

In addition to active sensing, using an energy harvester as an energy supply, driving the sensor module is another way to realize wearable self-powered sensing. This mode is mainly used for monitoring physiological indexes of the human body, such as glucose, urea, NH4+, and pH.

In Fig. 3e , Kim et al. proposed a self-powered thermoelectric glucose sensing system integrating a wearable thermoelectric generator (WTEG) and an emerging Li-S battery 88 . WTEG can provide a sufficient power output (378 μW) to drive the commercial glucose sensor and store the remaining energy in the Li-S battery. It can provide a stable energy supply even when the power supply and demand fluctuate greatly. This work demonstrates for the first time the feasibility of fully utilizing human body heat to drive commercial sensors.

Figure 3f presents a self-powered glucose sensing smart watch based on photovoltaic cells 89 . The energy collected by photovoltaic cells can be used to drive the sweat glucose sensor and enable real-time and in situ data analysis/display for driving e-ink screens.

The human body is a complex physiological system. In addition to glucose, the physiological indicators closely related to human health include urea, NH4+, pH, etc. Therefore, it is of great significance for real-time human metabolic health monitoring to realize the self-powered sensing of multiple physiological indicators at the same time.

As shown in Fig. 3g , W. Gao’s research group recently proposed a flexible perspiration-powered electronic skin that uses human sweat to generate electricity 90 . The unique integration of the zero- to three-dimensional nanomaterials helps the device reach a record breaking power density of 3.5 mW cm −2 for biofuel cells in untreated human body fluids (human sweat). This electronic skin has the ability to perform multiplexed metabolic sensing in situ and can wirelessly transmit the data to a user interface using Bluetooth technology. Such a self-powered sensor system with a high energy output and a multiple indicator monitoring capability has important application value in human metabolic monitoring, early diagnosis of diseases and other scenarios.

At present, the function of self-powered sensing systems has been greatly enriched. However, facing the obstacles of long-term stability, multimode sensing ability, and energy harvesting efficiency, the self-powered energy system has a long way to go before it can be used in large-scale applications.

Self-powered actuation system

In addition to wearable sensing functions, self-powered systems can also realize actuations such as object transportation, automatic control, drug delivery and release, adjuvant therapy, and microrobot control. In this section, some actuation applications of self-powered systems are briefly summarized.

Electrowetting refers to changing the wettability of droplets on the substrate by changing the voltage between the droplet and the insulating substrate, that is, changing the contact angle to make the droplets deform and shift. This process is often used to control the position and velocity of a fluid in microfluidic systems. Figure 4a shows a self-powered microfluidic transport system combining electrowetting with a TENG 91 . Here, a miniature vehicle composed of four droplets can transport small objects on the track electrodes when driven by a TENG. Such a self-powered transport system shows great applications in the fields of microsolid/liquid manipulators, drug delivery systems, microrobotics, etc.

figure 4

a Self-powered microfluidic transport system combining electrowetting with a TENG. Reprinted from ref. 91 with permission. b Flexible implantable electrical stimulator composed of a TENG and a flexible interdigital electrode. Reprinted from ref. 92 with permission. c Self-powered drug delivery system based on enzyme biofuel cells. Reprinted from ref. 93 with permission. d Self-powered electrofluorochromic devices in intelligent windows. Reprinted from ref. 94 with permission. e Micro self-powered robot. Reprinted from ref. 95 with permission.

In addition, TENGs also play an important role in clinical adjuvant therapy. Tian et al. presented a flexible implantable electrical stimulator composed of a TENG and a flexible interdigital electrode, as shown in Fig. 4b 92 . This electrical stimulation can significantly promote the adhesion, proliferation, and differentiation of osteoblasts and increase the intracellular Ca2+ level. Obviously, this self-powered electrical stimulation system shows great application potential in implantable medical devices and the clinical treatment of osteoporosis and osteoporosis-related fractures.

The transportation and release of drugs without an external power supply is also an important part of intelligent medicine. As shown in Fig. 4c , Xiao et al. proposed a self-powered drug delivery system based on enzyme biofuel cell 93 . Upon discharging the biofuel cell, the drug doped with a conductive polymer will be released rapidly. This work demonstrated the control and ex situ use of ibuprofen, fluorescein, and 4′,6-dimediyl-2-phenylindole (DAPI) and confirmed the feasibility of self-powered technology in drug transport and release scenarios.

Self-powered actuation systems also have important application value in smart homes. Figure 4d shows the application of self-powered electrofluorochromic devices in intelligent windows 94 . Perovskite solar cells can convert solar energy into electrical energy and rapidly color electrochromic materials.

The work in Fig. 4e shows a micro self-powered robot that can walk on the surface of water 95 . Superhydrophobic wires as artificial legs can obtain electric energy from water by redox reactions to supply power to the micromotor and drive the microrobot to move on water. Such a way of driving a robot could provide open prospects for developing self-powered microrobots in aquatic environments.

Self-powered actuation systems play important roles in medical, industrial, and household applications. With the development of processing technology, self-powered actuation systems are expected to undertake more sophisticated and complex functions in the future and further liberate human hands.

Portable and wearable self-powered smart system

In the era of big data and artificial intelligence technology, electronic devices are no longer limited to a single sensing or actuation function. It is an inevitable trend for portable and wearable electronic devices to develop toward the direction of intelligence 96 . In the future, wearable electronic devices with powerful data processing capabilities will become a new interface of human–machine interaction. In this section, we introduce the application of wearable self-powered systems in human–machine interaction scenarios and then show the synergy of wearable self-powered systems and machine learning technology.

In Fig. 5a , Guo et al. proposed a multifunctional electronic skin based on the synchronized triboelectrification and piezoelectric effect 97 . Through the design of network cross electrodes, the electronic skin can identify the motion trajectory in a full plane. A smart anti-counterfeiting signature system with the capability of recognizing the writing habits of people is realized and demonstrates its great application potential in a human–machine interaction interface and intelligent recognition.

figure 5

a Multifunctional electronic skin as a smart anti-counterfeiting signature system (SASS). Reprinted from ref. 97 with permission. b Triboelectric touch-free screen sensor (TSS). Reprinted from ref. 98 with permission. c Glove-based multifunctional human–machine interface based on TENG. Reprinted from ref. 99 with permission. d Self-powered triboelectric flex sensor (STFS) for sign language interpretation. Reprinted from ref. 100 with permission.

However, the human–machine interface based on the two-dimensional plane mentioned above can only detect limited kinds of gestures, such as touching and sliding. When using the three-dimensional space as the human–machine interaction interface, we can have an increased freedom of detection. Figure 5b shows an awesome concept of a noncontact human–machine interaction 98 . Here, a triboelectric touch-free screen sensor for recognizing various gestures in a noncontact mode shows the ability to detect multiple gestures, such as the drop and lift of a finger with different speeds, making a fist, opening a palm, and flipping the palm in different directions. In addition, this work shows a noncontact screen control system for smart phones, which presents a new touch-free design concept to develop the next generation of screen sensors.

The human hand is an important channel through which people express information. Through the development of wearable electronic devices that can be attached to the hand, it is possible to use rich hand movements to achieve a variety of interactive functions. Figure 5c shows a glove-based multifunctional human–machine interface based on TENG 99 . By matching the output signals under different gestures with specific functions, this human–machine interface is endowed with various functions, such as wireless car control, wireless drone control, minigame control, and VR game control.

In Fig. 5d , Maharjan et al. developed another self-powered triboelectric flex sensor that can be attached to a finger 100 . Additionally, based on the monitoring of a finger posture, this work shows a real-time application of sign language interpretation. It can be predicted that in the future, there will be more interactive functions beyond imagination, which can be realized through a self-powered human–machine interface.

Artificial intelligence technology represented by machine learning has infused vigor into the development of wearable electronic devices, which enables wearable electronic devices to realize more complex and accurate functions.

For example, a self-powered machine learning glove based on a superhydrophobic triboelectric fabric structure is proposed in Fig. 6a 101 . With the aid of a machine learning algorithm (convolutional neural network), the glove can achieve very accurate virtual reality/augmented reality (VR/AR) controls, including gun shooting, baseball pitching, and flower arrangement.

figure 6

a Self-powered machine learning glove based on a superhydrophobic triboelectric fabric structure. Reprinted from ref. 101 with permission. b Self-powered triboelectricity-based touchpad (TTP). Reprinted from ref. 102 with permission. c Epidermal sEMG tattoo-like patch for silent speech recognition. Reprinted from ref. 103 with permission. d TENG-based smart electronics for both voice signal recognition and handwritten signal recognition. Reprinted from ref. 104 with permission.

The self-powered triboelectricity-based touchpad (TTP) presented in Fig. 6b 102 has the same basic function as the electronic skin based on the piezoelectric-triboelectric principle 97 . However, the introduction of a pretrained neural network greatly enhances the accuracy and real-time trajectory recognition. Therefore, the TTP can be implemented as the smart calculator by using graphical user interface modeling.

The next generation of the computer revolution is from a graphical interface to a voice user interface, so voice recognition technology is a very competitive development direction in human–machine interaction.

Surface electromyography (sEMG) generated from the jaw contains valuable voice information. Therefore, Liu et al. demonstrated an epidermal sEMG tattoo-like patch with the ability of sEMG recording for silent speech recognition, as shown in Fig. 6c 103 . With the aid of wavelet decomposition and pattern recognition, the average accuracy of action instructions can reach 89.04%, and the average accuracy of emotion instructions is as high as 92.33%. Based on this performance, this tattoo-like patch can even be expected to help aphasia patients regain the ability to communicate with others.

Similarly, the TENG-based smart electronics in Fig. 6d has the ability of both voice signal recognition and handwritten signal recognition 104 . The “medium Gaussian support vector machine” is used as the machine learning model, and a recognition accuracy of 93.5% is obtained.

In summary, the combination of a self-powered system and machine learning technology makes wearable electronic devices more intelligent. It is worth mentioning that in the current work, self-powered intelligent systems mainly possess the two functions of recognition (Figs. 5a , d and 6b , d ) and control (Figs. 5b, c and 6a , c ), playing the role of assisting humans. In future developments, we can even devote ourselves to empower traditional electronics to “think,” “analyze,” and “decide” and realize the real integration of electronic devices and the human body.

Summary and perspective

Self-powered systems show great potential in energy harvesting, sensing, actuating, and human–machine interaction applications and are expected to become the main form of electronic devices in the Internet of Things era. In this review, we give a comprehensive introduction to the development of portable and wearable self-powered systems. We started with portable and wearable energy harvesting technology as the basis of self-powered systems and then introduced the application of self-powered systems in the wearable sensing of a series of physical and physiological indicators. In addition, we also introduced typical self-powered systems with actuating functions. Intelligentization is an inevitable trend in the development of electronic equipment. Therefore, we focus on the state-of-the-art developments of portable and wearable self-powered systems as a new type of human–machine interaction interface and demonstrate the importance of machine learning in empowering self-powered systems with higher-level functions. At the same time, we also discussed the application characteristics of different forms of portable wearable self-powered systems in different scenarios.

However, before the portable and wearable self-powered system can move toward large-scale practical applications, there are still many problems that should be fully addressed in the near future (Fig. 7 ).

figure 7

Challenges and prospects of portable and wearable self-powered systems.

Most wearable and portable self-powered systems are based on flexible materials, which experience device performance degradation during long-term operation. Therefore, in the future, we should study additional material and structural designs to improve the stability of the system under long-term work while ensuring the wearability of the device.

Currently, portable wearable self-powered electronic devices are mainly desktop laboratory devices, which only demonstrate a concept. Since there is no work to propose a standardized manufacturing process for portable and wearable self-powered electronic devices, it is impossible to achieve complete consistency with respect to the performance of two different devices. Therefore, in the future, we need to consider the issue of performance calibration between different devices, or we can develop standardized processes for portable wearable devices that can be mass produced.

Hybrid energy harvesting technology that integrates multiple transduction methods is most likely to act as a power source for future self-powered systems. However, the large differences in the frequency, amplitude, and waveform of electrical power converted through different transduction methods make it an unsolved problem to develop power management technologies suitable for different energy harvesting methods. Furthermore, an increasing number of functional modules are being integrated into self-powered systems. We need to rationally design power management circuits to improve the energy conversion efficiency and achieve energy distribution among various functional modules. In addition, a wireless module is needed to realize the transmission of information.

In summary, portable and wearable self-powered systems have made significant progress and may become the main form of the next generation of electronic devices. The rapid development of materials science, processing technology, and smart technology will pave the way for wearable self-powered systems in practical applications.

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Acknowledgements

This work was supported by the National Key R&D Project from the Minister of Science and Technology, China (2016YFA0202701, 2018YFA0108100) and the National Natural Science Foundation of China (Grant No. 61674004).

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Xu, C., Song, Y., Han, M. et al. Portable and wearable self-powered systems based on emerging energy harvesting technology. Microsyst Nanoeng 7 , 25 (2021). https://doi.org/10.1038/s41378-021-00248-z

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DOI : https://doi.org/10.1038/s41378-021-00248-z

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Literature on Wearable Technology for Connected Health: Scoping Review of Research Trends, Advances, and Barriers

Tatjana loncar-turukalo.

1 Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia

Eftim Zdravevski

2 Faculty of Computer Science and Engineering, Saints Cyril and Methodius University, Skopje, North Macedonia

José Machado da Silva

3 Institute for Systems and Computer Engineering, Technology and Science, Faculty of Engineering, University of Porto, Porto, Portugal

Ioanna Chouvarda

4 Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece

Vladimir Trajkovik

Associated data.

The list of the identified relevant research articles from the three selected digital libraries.

Wearable sensing and information and communication technologies are key enablers driving the transformation of health care delivery toward a new model of connected health (CH) care. The advances in wearable technologies in the last decade are evidenced in a plethora of original articles, patent documentation, and focused systematic reviews. Although technological innovations continuously respond to emerging challenges and technology availability further supports the evolution of CH solutions, the widespread adoption of wearables remains hindered.

This study aimed to scope the scientific literature in the field of pervasive wearable health monitoring in the time interval from January 2010 to February 2019 with respect to four important pillars: technology, safety and security, prescriptive insight, and user-related concerns. The purpose of this study was multifold: identification of (1) trends and milestones that have driven research in wearable technology in the last decade, (2) concerns and barriers from technology and user perspective, and (3) trends in the research literature addressing these issues.

This study followed the scoping review methodology to identify and process the available literature. As the scope surpasses the possibilities of manual search, we relied on the natural language processing tool kit to ensure an efficient and exhaustive search of the literature corpus in three large digital libraries: Institute of Electrical and Electronics Engineers, PubMed, and Springer. The search was based on the keywords and properties to be found in articles using the search engines of the digital libraries.

The annual number of publications in all segments of research on wearable technology shows an increasing trend from 2010 to February 2019. The technology-related topics dominated in the number of contributions, followed by research on information delivery, safety, and security, whereas user-related concerns were the topic least addressed. The literature corpus evidences milestones in sensor technology (miniaturization and placement), communication architectures and fifth generation (5G) cellular network technology, data analytics, and evolution of cloud and edge computing architectures. The research lag in battery technology makes energy efficiency a relevant consideration in the design of both sensors and network architectures with computational offloading. The most addressed user-related concerns were (technology) acceptance and privacy, whereas research gaps indicate that more efforts should be invested into formalizing clear use cases with timely and valuable feedback and prescriptive recommendations.

Conclusions

This study confirms that applications of wearable technology in the CH domain are becoming mature and established as a scientific domain. The current research should bring progress to sustainable delivery of valuable recommendations, enforcement of privacy by design, energy-efficient pervasive sensing, seamless monitoring, and low-latency 5G communications. To complement technology achievements, future work involving all stakeholders providing research evidence on improved care pathways and cost-effectiveness of the CH model is needed.

Introduction

As the worldwide population grows and the access to health care is increasingly being demanded, real-time monitoring of various physiological signals has driven the research and development of diverse wearable and implantable systems. Connected health (CH) describes the new paradigm of a technology-enabled model of health and lifestyle management [ 1 ]. It is implicitly a multidisciplinary technology domain set up to provide preventive and remote treatments. CH relies on a digital information structure based on the internet, sensing, communications, and intelligent techniques, in support of health-related applications, systems, and engineering.

Wearables, as well as hearables (in-ear devices) and nearables (neighboring devices that interact with wearables) integrated into the wider concept of Internet of Things (IoT), are being considered the most likely technologies to transform future health care and lifestyles [ 2 , 3 ]. This revolution began with the smartphone, which is now becoming a widespread intrusive and ubiquitous technology. Most current wearables and nearables are equipped with different types of sophisticated sensors. Different types of sensors powered by advanced analytics are being explored to develop functionalities of truly portable medical laboratories. Seamless integration of these measurements in smartphone apps permits for targeted information to be delivered on time, enhancing the user experience in typical assisted living scenarios. The general acceptance, ease of use, and reliability of smartphones facilitates user adherence to different added value apps that allow filling a gap in the area of self-physiological sensing and fitness monitoring [ 4 ]. Wearable technology has become mainstream, with the most significant influence on fitness and health care industries [ 2 ].

The importance gained by wearables among consumer devices can be tracked by their increasing share in consumer electronics shows promoting self-care and health management. According to the International Data Corporation, 172.2 million wearable units were shipped in 2018 [ 4 ], and this number is expected to grow, contributing significantly to the revolution of the IoT market [ 5 ]. Advances in wearable technologies and user acceptance of available consumer wearable devices pave the pathway toward seamless physiological monitoring.

The first body area networks (BANs) and wearable units comprised a number of sensors with a processing unit and wireless nodes assembled on printed circuit boards [ 6 , 7 ]. The design was bulky and uncomfortable, accompanied by large batteries, and had numerous issues associated with frequent recharge and loss of data communication. Since then, tremendous progress has been made in sensing technologies. The bulky design is being rescaled to a system on chip. Lowered power consumption, reliable communications, distributed processing, and data analytics improved the potential of wearables and made a significant impact on technology acceptance [ 7 ]. The technology innovations directly responded to user-related concerns (sensor miniaturization, seamless monitoring, secured communications, lower power consumption, energy harvesting, and plug-and-play functionalities) as well as safety and security (reliable sensing and data preprocessing, secured data communication, and reliable analytics).

However, the user feedback reviews report that initial user enthusiasm on wearables is often lost because of unclear use cases (unclear end user need), price, and associated complexities in device pairing with a smartphone [ 8 ]. The translation to long-term commitment to wearables requires clear use scenarios, valuable feedback, and constructive recommendations [ 8 , 9 ]. The inevitable transformation from a traditional, reactive health care model to a proactive and preventive model will bring clear use cases of CH solutions for early diagnostic or chronic condition monitoring [ 1 ]. Innovative CH scenarios are strongly motivated, exact, and economically beneficial [ 3 , 10 ].

The role that sensing, and information and communication technologies have gained as essentials in digital health has been summarized and elaborated in numerous research articles on sensors, data analytics, and secure and reliable communication platforms for CH solutions [ 3 , 10 - 16 ]. To stimulate and facilitate knowledge transfer and dissemination among policymakers and stakeholders, it is equally important to summarize those original findings with respect to specific application scenarios and specific user groups. Systematic review studies deliver such overviews based on an exhaustive manual screening of available digital libraries, providing a qualitative analysis of included studies, and unbiased performance comparison of the corresponding CH solutions [ 17 , 18 ]. The examples of such review studies offering a useful insight into the spectra of the related wearable technologies, target user groups, and application domains are plentiful. Wilde et al [ 19 ] reviewed the usage of apps or wearables for monitoring physical activity and sedentary behavior and emphasized the barriers and facilitators for their adoption. A scoping review [ 20 ] summarized the practices and recommendations for designing, implementing, and evaluating mobile health (mHealth) technologies to support the management of chronic conditions of older adults, considering articles published from 2005 till 2015. Kvedar et al [ 10 ] focused on the concept of CH as an overarching structure for telemedicine and telehealth and provided examples of its value to professionals and patients. In the study by Liu et al [ 21 ], materials, design strategies, and powering systems applied in soft electronics were reviewed. It also summarizes the application of these devices in cardiology, dermatology, electrophysiology, and sweat diagnostics and discusses the possibilities for replacement of the corresponding traditional clinical tools.

The transformation of the wearable landscape in the last decade is thus evidenced in a plethora of original articles and patent documentation and summarized and compared in numerous focused systematic reviews [ 3 , 10 - 16 , 19 - 21 ]. In this paper, we scoped the wearable technology field over the decade, starting from 2010 to February 2019, to identify trends in literature with respect to 4 important pillars: technology, safety and security, prescriptive insight, and user concerns. The collected literature reflects on the achieved progress, open issues, perspectives, and gaps in the development of wearable systems for future CH domain. The covered topics mainly relate to enabling technology: sensing, data aggregation and processing, communication protocols, power supply, data protection, and data analytics. However, the results of numerous pilots and experience gained with consumer wearables provide an insight into different user-related concerns. After exploring the literature published over the last decade, we have summarized state-of-the-art technologies, future research focus, and paper statistics related to the following key issues: enabling technology topics, application of wearable sensors in CH, and different user concerns.

With the more general, high-level perspective on the research on wearable technology, user-related concerns and challenges experienced over broad application area, this scoping review aimed at overlooking research trends unconstrained to a particular user group, health condition, or lifestyle scenario and including both mHealth and smart living environments. The extensive search scope is supported by automated search procedures relying on natural language processing (NLP) algorithms. The trends over the last decade were analyzed using a set of identified articles from 3 large digital libraries.

Purpose of This Review

Many studies elaborating on the use of sensors and wearables in assisted living environments, CH, and wellness and fitness apps were published in the last decade [ 3 , 10 - 16 , 19 - 22 ]. Those studies provide significant input for designing future CH systems, indicating benefits, but also shortcomings, barriers, and user feedback [ 19 , 23 - 29 ]. Nevertheless, there is a lack of studies with a general overview of the nature and extent of published research in that context.

This study aimed to identify and scope the scientific literature related to wearables in health monitoring, as measured by trends in the research evidence available in 3 large digital libraries: Institute of Electrical and Electronics Engineers (IEEE), PubMed, and Springer. The study scoped the field from several perspectives aiming to capture key drivers and major constraints in the deployment of wearable technology for health. The enabling technology relies on advances in sensing, processing, communications, and data protection. Conversely, multiple user perspectives imply privacy, utility, complexity, price, relevance, reliability, and significance of delivered feedback.

The objective of this study was to scope the research on wearable technology for health with regard to the following research questions:

  • What are the most significant research trends and milestones on wearables seen as an enabling technology and as a key driver facilitating CH solutions?
  • What are the most critical identified barriers and concerns from the technology and user perspectives and what trends are reflected in the research literature relating to these issues?

As an added value, this review can help identify the topics that need more detailed research in terms of elaboration of the obstacles and potential breakthroughs. The list of relevant articles resulting from this study can be filtered with respect to different fields (eg, keywords) to identify articles of interest for a systematic review in a specific subfield. The details in the list facilitate fast manual screening and selection of the subset of articles for further qualitative analysis. This type of preliminary search in planning a systematic review provides valuable answers on the feasibility (ie, does any evidence in literature exist), relevance (ie, has a similar systematic review already been done), and amount of time needed (ie, volume of the found evidence) to conduct a systematic review.

Scoping Review Methodology

This study adopted a scoping review methodology to identify and process the literature on wearables published from January 2010 to February 2019. Using a scoping technique, we aimed to examine the research evidence in the broad field of wearables, analyzing technology trends, including the resolved and emerging issues. The lack of a qualitative analysis of identified papers, the broad topic range, and the number of studies involved defined our approach as a scoping review and differentiated it from a systematic review [ 30 , 31 ]. The purpose of this study fully complies with the aims of a scoping review “to search, select and synthesize the knowledge addressing an exploratory question to map key concepts, types of evidence, and gaps in research,” as defined by Colquhoun et al [ 32 ]. Systematic reviews in the field of wearables, for its breadth and depth, have to focus more narrowly on wearable solutions and user concerns in a prespecified application scenario to facilitate qualitative analysis of included studies.

All emerging review types share their basis in scientific methodology, that is, they rely on formal and explicit methods for search and assessment of published studies and synthesizing of research evidence in conclusions on a well-defined research question [ 17 ]. One of the protocols for systematic reviews in health care, the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) [ 18 ], provides a good example of thorough and rigorous checklist guidance. The corresponding PRISMA flow diagram illustrates the information flow reflecting the number of studies in different systematic review stages: study collection, study scanning, eligibility evaluation, thorough qualitative synthesis, and quantitative synthesis in meta-analysis [ 18 ]. The methodological framework for scoping reviews is underpinned by this exact and transparent way systematic reviews are conducted [ 17 ], providing sufficient details to reproduce the results. The workflow for a scoping review proposed by Arksey and O’Malley [ 30 ], and adopted in this study, includes 5 stages:

  • Identification of a research question;
  • Identification of relevant studies;
  • Study selection;
  • Charting the data; and
  • Collating, summarizing, and reporting the results.

The identification of relevant studies and study selection stages in the scoping review methodology corresponds to the PRISMA workflow phases: study collection, scanning, and eligibility evaluation. To ensure transparency, we have enclosed the workflow chart to illustrate the number of identified, scanned, and included articles in this scoping review ( Figure 1 ).

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Object name is jmir_v21i9e14017_fig1.jpg

The review workflow reflecting the number of articles identified, screened, processed and removed in each step. The remaining articles were used for aggregating and summarizing the results.

The scope of this study was substantial and the collected research evidence on wearables surpassed the potentials of a manual search. Relying on the advances in NLP algorithms, the NLP tool kit [ 33 ] was used to ensure an efficient and exhaustive search of the literature corpus. The NLP tool kit is designed to automate the literature search, scanning, and eligibility assessment in the PRISMA methodological framework for systematic reviews [ 18 ], which are aligned with the scoping review phases: identification of relevant studies and study selection.

In the following sections, we clarify the usage of the NLP tool kit for study identification, selection (ie, scanning procedures and eligibility criteria assessment), and charting the data. It is worth noting that no quality assessment of the selected articles has been conducted, as this review has a scoping character. Instead, in the final step, we collated, summarized, and reported the results by aggregating the included studies to address the objectives of this review.

Setting Up the Natural Language Processing Tool Kit

This stage concerns the development of a plan comprising decisions on which digital libraries will be queried, relevant time span, suitable keywords, and properties that should be satisfied. This scoping review has employed the NLP tool kit [ 33 ] enabling both automated search, scanning, and processing procedures. The NLP tool kit ensures compliance with the terms of use of the digital libraries, with regard to the number of requests per unit time.

The NLP tool kit input parameters are a collection of keywords that are used to identify potentially relevant articles and a set of properties that should be satisfied by the identified articles. The input is further expanded by proposing synonyms to the search keywords and properties. Synonyms can be provided by the user or proposed by the tool kit and fine-tuned if needed.

Keywords are search terms or phrases that are used to query a digital library (eg, “health” and “ambient and assisted living,” “health” and “enhanced living environment”). Eventual duplicates in the results are removed in a later phase. Properties are words or phrases that are being searched in the title, abstract or keywords section of the articles identified with the keywords. Examples of such properties employed in this study are “monitoring,” “recommendation,” and “detection.” Property groups are thematically, semantically, or otherwise grouped properties for a more comprehensive presentation of results. For example, the property group for the set of properties given in the example above can be “information delivery.” Table 1 summarizes the relevant input categories used in this work.

The natural language processing tool kit input parameters: keywords, properties, and property groups.

Start year indicates the starting year of publishing (inclusive) for the papers to be included in the study. End year is the last year of publishing (inclusive) to be considered in the study. This review encompasses studies published from January 2010 to February 2019. Minimum of the relevant properties is a number denoting the minimum number of properties that an article has to contain to be considered as relevant. In this study, this value was set to 3, providing a right balance between falsely identifying relevant papers and potentially missing a relevant paper.

When researches perform a scoping review according to the above-mentioned methodology, the actual tasks they perform involve searching digital libraries with different search phrases, often involving complex Boolean conditions. The NLP tool kit counterpart to these phrases are the keywords described above. By screening the title and abstract, a reviewer determines whether the article is indeed relevant for the study. In the NLP tool kit, this process is automated using the properties and their synonyms to define what we are looking for in an article. Articles that contain more properties are considered as more relevant. Undoubtedly, a human reader might better understand the context and better assess the relevance of an article. However, the NLP tool kit is mimicking these tasks, but in an automated and more thorough way, providing incredible efficiency of the scoping review process. For more information about the actual implementation, we refer the reader to the study by Zdravevski et al [ 33 ].

Identification of Relevant Studies

Upon provision of the defined input categories, the literature search was started using only the specified keywords to query the selected digital libraries. The NLP tool kit indexed the following digital libraries (ie, sources): IEEE Xplore, Springer, and PubMed. It is worth noting that the NLP tool kit has used search engines of the corresponding publishers and retrieved the search results. Depending on the digital library in each search, the number of retrieved articles was constrained. In the PubMed library, all articles matching the given search criteria were retrieved for further analysis. The IEEE’s search engine limits the number of articles in each search to 2000, all of which were retrieved. For Springer, the search for each keyword separately is limited to 1000 articles or 50 pages with results, whichever comes first, sorted by relevance determined by Springer.

Study Selection

After articles had been identified based on the specified keywords and retrieved from the publishers, the study selection (screening and eligibility assessment) procedures followed.

Upon merging the results from multiple independent keyword-based searches, some articles could be present multiple times because they could be identified by different keywords or in multiple libraries. Therefore, the collected articles were screened, and duplicates were removed using their digital object identifier (DOI). In addition, the screening process discarded articles that were not published in the required time span (ie, last 10 years) or for which the title or abstract could not be analyzed because of parsing errors, unavailability, or other reasons.

The selection of studies from the remaining subset of articles relied on the advanced functionalities enabled by NLP tools. The NLP tool kit automates analysis of a title and abstract for each study, significantly reducing the number of articles for manual screening. The automated eligibility analysis involved the following processing: tokenization of sentences [ 34 , 35 ] and English stop words removal, stemming, and lemmatization [ 35 ] using the Natural Language Tool kit library [ 36 ]. Stemmed and lemmatized properties were searched in the cleaned abstracts and titles, and each article was tagged with the properties it contained.

The processed articles were selected (ie, labeled as relevant) if they contained at least 3 of the predefined properties in its title or abstract (considering the above NLP-enhanced searching capabilities, thus performing a rough screening). To help in the eligibility analysis, the selected articles were sorted by the number of identified property groups, number of identified properties, number of citations (if available), and year of publication, all in descending order. For the relevant articles, the tool kit automatically generated a bibliographic file (as defined by BibTeX reference management software) for simplified citations.

The information flow diagram illustrating the numbers of identified, screened, processed, and removed studies in the automated NLP procedure is presented in the Results section ( Figure 1 ) to ensure transparency and reproducibility.

The listing of the relevant identified articles extracted from IEEE, PubMed, and Springer is available in Multimedia Appendix 1 as an Excel file with the following fields: DOI, link, title, authors, publication date, publication year, number of citations, abstract, keyword, source, publication title, affiliations, number of different affiliations, countries, number of different countries, number of authors, BibTeX cite key, number of found property groups, and number of found properties. These additional files facilitate refined manual search of the articles with specific filtering criteria. The subset of targeted articles can subsequently be retrieved from their publisher and manually analyzed for potential inclusion in the qualitative and quantitative synthesis. It should be noted that not all the references provided within this study are from the identified set of relevant papers. Some additional papers identified in a manual search were used to illustrate and confirm the findings of this scoping review. However, these referenced papers from other libraries have not been used to identify trends in this scoping review.

To replicate the results manually, the keywords in Table 1 have to be used to inquire the selected digital libraries using their search engines. The properties serve for identification of the relevant articles by scanning titles and abstracts of the identified studies. The results can be compared with the resulting list of included studies, provided in Multimedia Appendix 1 .

Charting the Data

To answer the research questions, we defined indicators to be extracted from the selected studies. The trends in the past decade were analyzed relying on a broad scope of literature. The processed and retained relevant articles were aggregated by several criteria:

  • Source (digital library) and relevance selection criteria;
  • Publication year;
  • Digital library and publication year;
  • Search keyword and digital library;
  • Search keyword and year;
  • Property group and year;
  • Property and year, generating separate charts for each property group; and
  • Number of countries, number of distinct affiliations and authors, aiming to simplify the identification of collaboration patterns (eg, written by multiple authors with different affiliations).

These aggregated metrics are available in the form of comma-separated values files and charts. The plotting of the aggregate results was integrated and streamlined using the Matplotlib library [ 37 ] and NetworkX [ 38 ]. The NLP tool kit enables graphical visualization of the results, where each node represents one of the properties, each edge connects 2 different properties, and its weight is determined by the number of articles containing both properties connected by that edge. Articles that do not contain at least 2 properties, and properties that were not present in at least 2 articles were excluded. For a clearer visualization, only the top 25% property pairs by the number of occurrences are shown in a graph.

A similar graph for the countries of affiliations was generated. The top 50 countries by the number of collaborations were considered for this graph. Countries and an edge between them are shown if the number of bilateral or multilateral collaborations was in the top 10% (above 90th percentile) within those 50 countries.

Collating, Summarizing, and Reporting Results

Using charted data and extracted evidence, we were able to analyze the trends in data and provide qualitative analysis for each thematic segment (as defined by the property groups). The results were reported with regard to the raised research questions. The meaning of these findings was related to the study purpose, and the potential impact on the future research direction was discussed.

Number and Distribution of Identified Articles

Using the NLP tool kit and searching 3 digital libraries: PubMed, IEEE, and Springer, we identified 21,288 studies with potential relevance ( Figure 1 ). Duplicates that emerged in multiple independent searches were removed, reducing the total number to 15,218 studies. The first screening process further eliminated 5006 studies published before 2010 or for which the title or abstract could not be analyzed because of parsing errors, unavailability, or any other reason. The remaining 10,212 studies underwent an automated eligibility assessment using the advanced NLP tool kit functionalities. After processing, the articles were tagged with identified properties, and all articles containing less than 3 properties were removed. Overall, 2406 articles were deemed eligible for further manual inspection and inclusion in identifying the research trends and summarizing the results. The statistics on the number of the collected articles, duplicates, articles with invalid time span or the articles with incomplete data, and relevant articles are presented in Figure 2 for each digital library.

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Statistics on the number of articles in the identification and study selection (screening and eligibility assessment) phase for each digital library. IEEE: Institute of Electrical and Electronics Engineers.

The distribution of the number of collected and relevant articles per year is presented in Figure 3 . An increasing trend in the number of collected articles can be noticed from January 2010 to February 2019. The same trend is followed by the number of included articles, which rises from 136 in 2010 to 393 in 2018.

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The number of analyzed articles versus the number of selected relevant articles on wearable technologies per year from January 2010 to February 2019.

Combining the information on the digital library (source) and publication year of the identified relevant articles, the obtained distribution reveals that IEEE, being a more technology-oriented library, has an increasing trend in the number of relevant articles from 2010, peaking in 2017 ( Figure 4 ). PubMed leads in the number of articles dealing with CH and assisted living and covers more of the searched properties related to user concerns. The number of PubMed articles follows an increasing trend from 2010 and saturates in research evidence from 2016 onward. The Springer library shows an oscillating trend from 2010 to February 2019, with an average of around 50 articles per year.

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Object name is jmir_v21i9e14017_fig4.jpg

The number of identified relevant articles per year from January 2010 to February 2019, sorted by the originated digital library (Institute of Electrical and Electronics Engineers [IEEE], PubMed, and Springer).

Geographical Distribution and Collaboration Evidence

The authors’ affiliations were used to identify wearables’ research community clusters and eventual hubs at the research forefront. Multiple country associations were discovered, but for the sake of presentation clarity, the graph in Figure 5 shows 25 countries (nodes) and 56 edges with at least 7 joint articles (90th percentile) specified as edge weights. The number of papers per presented node is color coded, where violet corresponds to the higher and yellowish (paler) color to the lower number of articles. The identified hubs, United States, Canada, United Kingdom, Germany, China, and Italy, feature both national and international scientific production, whereas the strongest edges exist between the United States and Canada and between the United States and China. The collaboration patterns largely correspond to the neighboring geographical areas. The European countries demonstrate active collaboration scheme as well. The United States, United Kingdom, and China have significant national scientific production in the analyzed research domains.

An external file that holds a picture, illustration, etc.
Object name is jmir_v21i9e14017_fig5.jpg

The number of relevant research papers per country and collaboration links with the annotated number of joint articles. For clarity, only 25 countries (nodes) with significant research contribution and minimum 7 joint collaborations (edges) are presented.

Keywords Statistics

The selected keywords used to map the literature corpus on wearables with respect to the set research questions appear in the relevant articles with different distributions. Figure 6 presents the annual number of research papers identified by the search engines of 3 libraries with the defined keywords and additionally filtered manually based on their relevance to the defined properties. Please note that the internals of their search engines are not known, and the libraries might differ in the way they look for these keywords: only in a title, keywords section, abstract, or a whole article. Depending on the digital library, the ratio of the relevant papers containing specific keywords changes ( Figure 7 ). The IEEE digital library has a focus on enabling technology for CH, in terms of novelties in wearable sensing, data processing analytics, computing, and communication protocols. PubMed publications are also oriented toward CH technologies from an assistive and supportive perspective. Springer publications cover slightly different topics, focusing mainly on ambient assisted living (AAL) and ambient intelligence and generally contain more technical articles that address assistive technologies.

An external file that holds a picture, illustration, etc.
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Distribution of the number of relevant articles with each of the defined keywords on an annual basis from January 2010 to February 2019.

An external file that holds a picture, illustration, etc.
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The number of relevant articles containing each one of the keywords per digital library. The data are aggregated within the defined period. IEEE: Institute of Electrical and Electronics Engineers.

Statistics of Properties

As the number of research articles increases within the observed time frame, the number of articles dealing with associated topics summarized in property groups increases accordingly ( Figure 8 ). The increasing trend is accompanied by the stable ratio of papers, with technology-related publications being the leading in number, followed by research related to information delivery, safety and security, and user concerns. When the view is zoomed from property groups to properties, the graph reveals the centrality of monitoring as the essential function of a wearable system tightly connected with the key technology: sensing ( Figure 9 ). The 2 properties interrelate with communication, detection, reliability, safety, security, transmission, data analytics, and privacy as technologically empowered concepts. Acceptance is the key user-related property in the graph core, with privacy and protection to follow.

An external file that holds a picture, illustration, etc.
Object name is jmir_v21i9e14017_fig8.jpg

The number of relevant articles related to each property group per year within the predefined time frame from January 2010 to February 2019.

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Object name is jmir_v21i9e14017_fig9.jpg

The property graph indicates the number of relevant articles with each property and the co-occurrences of properties in those.

Principal Findings

Wearable medical devices play a critical role as an enabling technology and as a key driver that has facilitated the emergence of CH solutions. This paper presents an overview of the most important milestones and trends that have driven research and development initiatives on wearable technology domains in the last decade. Simultaneously, it aimed to identify the most critical barriers or concerns, as far as technology and user aspects are concerned, that hinder the generalized adoption of wearables and still require further research.

The adopted methodology used the NLP tool kit for searching in 3 digital libraries, PubMed, IEEE, and Springer, for papers that address research on wearable technologies for medical applications. In the following, we address the findings related to the research trends in technology, information delivery, user concerns, safety, and security.

Technology as a Key Driver

The literature ( Figure 10 ) reflects the intense research and development in sensor design, communication protocols, and data processing and analytics. The emergence and evolution of concepts of edge computing, cloud, and fog could be easily tracked. As technology is a key enabler of future CH systems, we briefly review significant technological advances in the comprising components of a wearable system.

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The number of relevant articles containing each property grouped primarily into the technological domain in the period from January 2010 to February 2019.

Evolution of Sensing Technology

Available sensors and their characteristics largely influence the design of CH systems. The direct sensor’s contact with the body implies their stiffness and size, as the most important features concerning comfort and measurement accuracy. The placement of wearable sensors influences their characteristics, user acceptance, and engineering requirements. As sensors evolve from wearable and implantable to ingestible sensors, barriers arise on multiple pathways: regulatory, technical, and translational [ 39 ].

The marked progress in wearable sensors is linked to advances in material science and embedded systems. Smart garments or electronic textiles, featuring sensor flexibility, made the first promise toward seamless and pervasive monitoring. The sensor integration into fabrics varies from garment level, assuming sensor integration at a later stage, to fabric level implying sensor integration by application of coatings to the fabrics [ 40 ]. The striving level is a fiber level [ 40 ] implying integration of conductive threads and fibers in the knitting process to result in a smart fabric (a concept first proposed about 20 years ago [ 41 ]).

Microcontroller-based systems can as well be included within different textile fabric for health applications [ 42 ]. Some products have already been approved and introduced to the market, but most of them are at a prototyping stage. The limitations arise at the electronic and textile integration step, slowing down technology transfer. In addition, there are multiple regulatory concerns, such as safety, reliability, and recycling [ 43 ]. Another promising technology for wearable CH solutions is microfluidics. Both sensing and drug delivery can be realized by combining microfabrication and liquid manipulation techniques with conductive elements on stretchable and flexible materials [ 44 , 45 ].

Low-power microelectronics, biocompatible materials, micro- and nano-fabrication, advances in data transmission, and management of sensor drift have driven the development of implantable biosensors [ 46 ]. Recent advances report the use of polyamide, flexible material for sensor platforms [ 47 , 48 ]. Research on flexible mechanical and electrical sensing has demonstrated great potential in in vitro diagnostics [ 49 ] and advanced therapy delivery [ 50 ]. Polymer-based switching matrices used for electronic skin to enable pressure sensing (robots, displays, and prosthetics), evolved into skin-attachable wearable electronic devices [ 48 ]. Another use-case involves surgical procedures, where these matrices are used in surgical procedures as part of mapping systems attached to the surface of the organs [ 50 ]. Active research directions in polymer sensors are focused on transparency [ 51 ], self-powering [ 52 ], and self-healing [ 53 ] capabilities.

The new generation of implantable sensing solutions for tissue and organ monitoring is enabled by advances in epidermal electronics based on soft lithography and thin-film sensors [ 46 , 54 ]. For example, electrocardiogram, blood glucose, and blood pressure sensors integrated with microstructures provide optical, thermal, and electrical stimulation [ 55 ].

Hearables are one of the latest wearable devices aiming to integrate sensing of multiple physiological signals into a single device [ 56 ]. The in-ear placement of such a device requires a flexible and comfortable fit and provides stable position regardless of the subject’s gross movements. The viscoelastic foam used as a substrate additionally ensures artefacts absorption, as the ear channel is affected by small movements, when speaking, swallowing, or chewing. The solution proposed by Goverdovsky et al [ 56 ] offers continuous measurements of cardiac, brain, and respiratory functions.

Implantable pacemakers, pressure sensors, cochlear implants, drug infusion pumps, and stimulators are all examples of implantable devices delivering therapy or providing physiological monitoring [ 39 ]. The majority of implantable devices currently operate in an open loop. New research challenges are focused on combining monitoring and therapy delivery for the optimized closed-loop personalized therapy [ 39 ]. The neural signal recording is ultimately the most demanding task, as it requires precise, low-power, and low-noise electronics and miniaturized and light weight implantable designs [ 57 ]. Neural implants face the hardest challenges in the translational pathway of the research-grade solutions into clinically approved products.

Ingestible sensors for image and data recording in gastrointestinal endoscopy have already proven their benefits in early detection of gastrointestinal cancers [ 58 ]. Ingestible, similarly to implantable devices, face challenges that shape the ongoing research: operation frequency selection, amplifiers, antenna design and performance, wireless channel modeling, increasing data rates, and power considerations.

Besides tracking basic physiological parameters (electrocardiogram, blood pressure, blood oxygen saturation, temperature, etc) sensing functions in wearable medical devices have also moved off the body toward contactless or seamless ambient embedded physiological sensing in, for example, keyboards, joysticks, steering wheels, bicycle handles, doors [ 59 ], mattresses [ 60 ], beds [ 61 ], and toilet seats [ 62 ]. The combination of such monitoring products with the data-driven services has promoted the development of the AAL concept. The AAL is a new ambient intelligence paradigm where new technologies are associated with the social environment, to transparently improve and assist the daily quality of peoples’ lives. Despite the high number of research and industry organizations already active in the AAL field, significant efforts are still needed to bring these technologies into a real-world usage [ 15 ].

Powering Wearables: Constraining Consumption and Energy Harvesting

One of the limitations for a widespread adherence to wearable electronic products concerns the power supply needs [ 7 , 9 , 63 ]. Active wearable systems need to be comfortable, light, user-friendly, and power efficient. The identified research trends reveal that research on battery technology lags compared with research on other wearable system components ( Figure 10 ). This implies that energy efficacy and efficiency remain an important design concern, both for wearable systems and in the design of networks to serve future landscape of wearables (notably fifth generation [5G] architectures).

Energy harvesting technologies have been explored as an alternative energy source to recharge power batteries or super capacitors. The ongoing research in this domain has investigated technologies to explore motion [ 64 , 65 ], thermal [ 66 , 67 ], optical, electromagnetic [ 68 ], solar [ 69 ], and chemical forms of energy [ 70 ]. However, miniature devices that can harvest proper levels of energy are still in their infancy.

Complementary efforts are being invested in the integration of power-efficient technologies and design techniques in wearable systems. Among those are energy-efficient and low-power wireless communication, voltage scaling, low-leakage and low-voltage complementary metal oxide semiconductors [ 71 ], and power-performance management.

Communication Protocols for Wearable Systems

The medical data are low in volume, but with strict requirements in terms of latency, link reliability, and security [ 7 ]. Wearable body sensor networks or BANs refer to sensor networks applied for acquisition or monitoring of vital physiological body parameters unobtrusively. These systems can be used in clinical settings or at home by patients or even healthy people who want to improve or monitor their health conditions.

BANs enable wireless communication in and around a human body in 3 different tiers: intra-BAN, inter-BAN, and the beyond-BAN. Intra-BAN communications refer to communications between on-body sensors, within the surrounding body area, enabling wireless data transmission to a personal server. According to the application and design parameters, the intranetwork can be wired or wireless, or even use the human body as a communication medium. Wired networks, as a second type of communication infrastructure for BAN applications, provide high-speed, reliable, and low-power solutions [ 72 ].

The international IEEE 802.15.6 standard enables delivering of low power, short range (in the vicinity or inside, within the human body) reliable wireless communications, with data rates from 75.9 kbps to 15.6 Mbps, making use of industrial, scientific, and medical bands, as well as frequency bands approved by national medical and regulatory authorities [ 73 ].

The inter-BAN communications include communicating data from personal devices such as smartphones to the access points, either in an infrastructure-based manner or in an ad hoc manner. Wireless BANs can interact with other existing wireless technologies such as ZigBee, wireless local area networks (WLAN), Bluetooth, wireless personal area network, video surveillance systems, and cellular networks [ 73 ].

Finally, the beyond-BAN tier connects the access points to the internet and other networks. Beyond-BAN architectures can be implemented in cloud or fog network infrastructures [ 74 ] implying protocols , cloud-based systems, and fog systems as research topics in the wearable CH domain. The major challenges in BAN are associated with media (path loss because of the body absorption), physical layer (minimization of power consumption with uncompromised reliability and interference), medium-access control layer (supporting multiple BANs in parallel application), security, and transmission (loss and delay sensitive real-time transmission) [ 75 ].

Limited spectra and the need for higher data rates drive the communication community toward the new generation of cellular networks such as 5G [ 22 , 63 ]. The high-speed data and low-latency features of 5G networks will allow wearable devices to communicate faster (in less than 1 millisecond) and perform real-time control. 5G will be a platform for various services and applications, with support to different communication requirements. The transition to millimeter wave (mmW) frequencies will require new communication architectures to be designed for specific mmW propagation. For protection and regulation of exposures to such frequencies, more appropriate metrics are needed, such as temperature elevation of the contact area [ 7 ].

The design of wearable antennas, with safety concerns, device-centric architectures, and smart device communication are some of the changes 5G will require. The development of 5G brought the promises supporting the wearables market, such as radio-frequency sensor charging [ 63 ], reduction in latency, high data rates and capacity, and network densification, enabling the massive number of deployed wearables per micro- or picocell [ 22 ].

The 5G architectures proposed to serve wearables include microbase stations for blanket coverage, whereas local coverage and data throughput should be ensured with small base stations and remote radio headers (RRHs) [ 7 ]. RRHs can also support different wireless technologies to ensure backward compatibility (Bluetooth, visible light communication (VLC), etc). The connection to cloud data servers via base stations enables storage, retrieval, and analytics of user-specific data. Realization of communications between wearables and network edge nodes can be done using licensed or unlicensed communication bands. Licensed communication bands provide quality of service at an increased cost at several levels: a service provider cost for more expensive licensed chips and more power consumed on licensed communication protocols. Unlicensed communication (eg, Bluetooth, WLAN, and VLC) is a cheaper, power-preserving option but limited in range [ 7 ].

Data Processing and Analytics

The large volume and heterogeneous data types collected using wearable technology have grown beyond the abilities of commonly used data processing techniques [ 76 ]. The necessity for reducing the volumes of captured data at the source, to reduce the power consumption and latency, brought processing closer to the sensor nodes, mapping the data algorithms to ultralow-power microcontrollers [ 46 ]. Preprocessing approaches, such as noise filters, peak detection, and feature extraction, allow for significant data reduction at the source [ 77 ]. Conversely, advanced data analytics imply sensor data integration, thus relying on the powerful devices located in the cloud. 5G should offer mobile edge computing to reduce latency and traffic demands to the central node. In the wearable scenario, communication between various user devices is fostered by 5G machine-to-machine communications, enabling local processing, low latency, and power saving [ 7 ].

High-performance computing permits efficient processing of large data volumes through a map-reduce framework [ 78 ]. Advanced data caching and in-memory processing coupled with GPU accelerators and coprocessors support intensive parallel operations. The availability of higher computational power enabled the rebirth of computationally intensive deep neural networks, resulting in superhuman performance and cutting-edge research in multiple domains. These are enabling technologies that will bring to reality the third generation of pervasive sensing platforms [ 46 ] that will integrate and extract information from a variety of sources: sensed data, clinical records, genomics, proteomics, and social networks, leading to a system-level approach to human health [ 79 ].

Information Delivery and Valuable Feedback

The research in the user-associated information delivery is primarily concerned with recommendations, provision of feedback, and real-time user insight ( Figure 11 ). Current commercial wearable technologies, tracking vital signs and patterns of activity, lack the relevance for many potential consumers, presenting an additional burden [ 7 ]. The motivation to buy and use wearable systems has to be justified in a functional CH application context. The clear user benefit comes from a validated system that would transform collected data into manageable and useful information for medical action, safety instructions, or self-performance estimation and improvement.

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The number of relevant articles related to recommendations and feedback on health monitoring solutions from January 2010 to February 2019.

To gain wider consumer preference, the information generated by wearables has to be fitted into specific contexts, offering the needed insight and recommendation on actions that should be taken. The second generation of wearable systems, which aims to enable context sensing, needs to integrate many different types of context information, such as sensor information, user profiles and preferences, activity patterns, medical history, and spatial information (location and environment conditions). If not strictly depending on medical condition, the timing, content, and frequency of prompting have to be adjusted to user preferences [ 80 ]. As a basic example, the time of day or night implies different content and presentation of the prompting messages because of the different level of user’s readiness and wakefulness [ 81 ]. The fusion of physiological and context sensing data will rely on sophisticated data analytics for extraction of relevant information and decision making on an action to be prescribed or advised to the user. The feedback to prompting messages generated in day-to-day system’s interactions with a user would ensure the adaptation to user preferences in time, relying on reinforcement learning.

The transformation of wearables from measurement devices into resources of reliable real-time information, history mining, and smart and personalized decisions would qualify them for health and performance monitoring solutions.

Wearables will reshape individuals and society, promoting self-care and health management, moving care outside hospitals, affecting enterprises, and revolutionizing health care [ 8 , 82 , 83 ]. Their seamless integration into consumers’ electronics is well witnessed throughout the Consumer Intelligence Series on wearables from 2014 and 2016 [ 8 , 83 ]. According to these sources, numerous user concerns such as design, accuracy, reliability, security, privacy, and dampened human interaction are becoming less worrying to the users. Research on sensor materials and communication solutions can provide advances in human-centered design and enhance the user experience.

Another big hurdle for deploying wearable systems in the real-world concerns technology acceptance [ 84 , 85 ]. Even though wearables are adopted by the millennials, the older population is still uncomfortable with using and relying on technology. As opposed to the smartphone, the use of wearables in fitness and well-being scenarios does not have clear usage need and benefits. Consumers complain about uncomfortable and unattractive design, short battery life, and frequent connectivity challenges [ 8 ]. With the first wearable devices, we have witnessed a wearables fatigue attitude, which is noticeable in a significant percentage of wearables being discarded within the first 6 months of use [ 86 ].

Our findings are aligned with the outcomes of the user feedback reviews on wearable technologies [ 8 , 83 ], as the identified articles confirm the steady increase in research addressing user-related issues such as technology acceptance, technology adoption, and privacy ( Figure 12 ). The primary design requirements are that a wearable device must be fit for the purpose and seamlessly adapted to the user’s lifestyle to be accepted.

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User-related properties and concerns addressed in the relevant literature corpus from January 2010 to February 2019.

Preserving privacy and confidentiality is a priority to be considered in design specifications. Communications should be encrypted and secured, and the involved parties should ensure confidentiality. This is particularly important in the case of wireless data communications that are easier to intercept [ 87 ]. Personal monitoring devices should unobtrusively authenticate the user identity using biometrics or key physiological signs (owner-aware devices).

Different user concerns, such as quality of experience, security, privacy, technology acceptance, and human-centered design, are relevant research topics in the wearable CH domain and can be used to identify future challenges and research trends. Although some of them (eg, quality of experience and human-centered design) might be decreased as the end user pool gains digital competence and technology matures with time, some of them (security, privacy, and technology acceptance) will probably evolve and mix with other, more societal research topics such as environmental impact, circular economy, and digitalization of society. These can raise a new set of concerns related to the socioeconomic impact of wearable technologies in combination with IoT and 5G technologies used for health care and lifestyle.

It is worth mentioning that another spectrum of concerns and barriers relates to the stakeholders involved in the provision and management of health care. Health professionals need scientific evidence on the reliability of collected data, the performance of analytical models mapping the collected data to disease progression, and eventually positive patient outcome in using wearable-based CH solutions [ 1 ]. Reshaping the health care critically depends on research work devoted to the design and evaluation of care pathways, provision of optimized feedback, and eventually providing evidence on long- and short-term cost-effectiveness of CH solutions [ 1 , 88 ].

Safety and Security

Safety and security are primary considerations for medical devices, tightly coupled with reliability at all system levels. Our findings confirm the increasing importance and research efforts related to these major user concerns ( Figure 13 ). If the wearable device is required to perform safety-critical functions, the tolerance for error is zero. A failure in such a device can cost a life and that requires more effort and time (ultimately cost) to be invested in thoroughly testing and validating the device before it is deemed safe to use.

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Safety- and security-related properties: protection, reliability, safety, and security. The trends in the period from January 2010 to February 2019 evidence their increasing presence (ie, relevance) as the wearable technology matures.

Along the life cycle of a wearable device, efficient mechanisms are required to detect and diagnose deviations occurring in the captured data. Correct differentiation of errors due to system-related faults from those due to a change in health status is a necessity. Increased level of false alarms (false positive) would prevent user reliance, reduce user alertness, and hamper user adherence to the provided feedback.

Both features, safety and security, are technology conditioned and should be ensured by the system design. Wearable medical devices are required to comply with IEC 62366-1:2015 standards [ 89 ] that regulate the application of usability engineering to medical devices to achieve approval. Two new EU regulations on medical devices issued in 2017 that will come into force in May 2020 are Regulation (EU) 2017/745 on medical devices [ 90 ] and Regulation (EU) 2017/746 on in vitro diagnostic medical devices [ 91 ]. These regulations will have a significant impact on the sector of medical devices that incorporates wearable technology. More stringent procedures for evaluation of medical devices and conformity should improve patient safety.

The CH paradigm involves more connectivity and communications into health care and medical devices. Any device connected to the internet is prone to be targeted for malicious purposes, putting it at a constant threat of damage, theft, and financial cost.

The number of detected data breaches in health care organizations has increased significantly in the last several years [ 92 ]. The reasons are not primarily technical but in part caused by the negligence and lack of knowledge of employees in treating this sensitive data and implementing the information security practices [ 92 ]. According to the 2017 Fourth Annual Data Breach Industry Forecast, health care organizations will be the most targeted sector with new, sophisticated attacks emerging. New security frameworks for mHealth are being proposed to ensure security and reliability of medical devices and personal health data [ 93 - 96 ].

After the General Data Protection Regulation was put in place on May 25, 2018, the requirements for data protection and privacy assurance have been raised and unified across Europe. The health care and monitoring systems have to adhere to the privacy by design principle, which requires the incorporation of privacy protection in systems design and not as an afterthought add-on solution.

Limitations of the Study

This study considered only 3 digital libraries, and some relevant articles from nonindexed publishers were not considered. However, keeping in mind the size of the considered digital libraries, we believe that the obtained results are indicative for the purpose of the study.

All digital libraries that were used in this work have different internal search engines with different rules for the maximum number of papers that can be retrieved and different formatting of search results. The papers obtained for this study are the results of the same search query sent to those different search engines. However, keeping in mind the number of papers that were analyzed within this scoping review, we believe that specificities of the publishers’ search engines have limited impact and have not influenced the findings of this work.

In the future, the NLP tool kit needs to be extended to process more digital libraries. In addition, there is an apparent need of a Web app that will make it available to a wider audience. Until then, readers are encouraged to contact the authors if they are interested in using the tool kit.

Wearable medical solutions, integrated into the wider concept of IoT, provide for pervasive data acquisition from a body and beyond, and rely on powerful data analytics, smart networking, and machine-to-machine communications to facilitate patient-centric, personalized, and holistic care. Although technological innovations and availability support the emergence of CH solutions, the widespread adoption of wearables is still hindered by numerous concerns related to reliability, security, and cost-effectiveness.

This scoping review maps the scientific literature related to wearable technology in health care starting from January 2010 to February 2019, identifying the research trends related to enabling technology, and the trends in addressing the concerns from both user and technology perspectives. The NLP tool kit supported search procedures applied over 3 large digital libraries, IEEE, PubMed, and Springer, which provided for a representative subset of 2406 articles on wearable technologies for medical applications.

On the basis of the investigated sample, the main findings reflect key drivers in the field, some research gaps and relevant topics that would benefit from more systematic qualitative knowledge synthesis:

  • User concerns were the least addressed topic, whereas the enabling technology research was the main focus in the literature within the observed time period;
  • Major breakthroughs were made in sensor technology, data analytics, communications, and computing architectures (edge and cloud);
  • Research on battery technology and efficient solutions for energy harvesting has lagged, implying energy efficiency as one of the major constraints in designing wearable solutions for pervasive monitoring;
  • Research on communication technologies focuses on 5G featuring low-latency, massive connectivity, and high capacity to mitigate the current challenges with respect to real-time feedback, energy, and computing constraints;
  • The research related to the user-associated information delivery was mainly focused on monitoring and measurement information and much less on the provision of feedback recommendation and prescriptive insight; and
  • The most addressed concerns from the user perspective were technology acceptance and issues related to safety and security, implying privacy and reliability as the most central topics.

This study confirms that applications of the wearable technology in the CH domain are becoming mature and established as a scientific domain. However, further research and development are required to improve their reliability, comfortability, and dependability levels. The research focus shifts from sensors and data analytics toward the sustainable delivery of valuable recommendations, reliable, energy-efficient, and low-latency communications and computation offloading. Sensor data integration goes beyond body-level integration to include context sensing, location and environment metrics, medical history, pattern of activities, and user preferences. This is essential for making wearables a robust patients’ representation interface and reliable node of the IoT infrastructure that makes CH a reality.

There is a further need to explore and provide the literature evidence supporting the positive experiences, improved patient outcomes, and cost-effectiveness of CH solutions. Practical adoption in the field still demands design and validation of new care pathways, optimization of interventional strategies, and a sound business model.

Acknowledgments

This article/publication is based upon work from COST Action ENJECT TD 1405, supported by COST (European Cooperation in Science and Technology; www.cost.eu).

Abbreviations

Multimedia appendix 1.

Authors' Contributions: TLT and VT conceived of the idea of scoping review, contributed to the scoping review, and drafted and edited the manuscript. EZ contributed to coding for the platform for scoping reviews and visualization of results. JMS contributed to the scoping review and editing of the manuscript. IC contributed to the scoping review methodology and editing of the manuscript.

Conflicts of Interest: None declared.

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