Recent Trends in Wearable Computing Research: A Systematic Review

Vicente J. P. Amorim, Ricardo A. O. Oliveira, Maur´ıcio Jose´ da Silva Computing Department (DECOM) Federal University of Ouro Preto (UFOP) Ouro Preto, Brazil Email: [email protected], [email protected], [email protected]

Abstract—Wearable devices are a trending topic in both com- are frequently used to continuously monitor users’ biometric mercial and academic areas. Increasing demand for innovation signals, being one of the most common uses of these products has led to increased research and new products, addressing new [8]. In addition, wearable solutions are being used in enter- challenges and creating profitable opportunities. However, despite a number of reviews and surveys on wearable computing, a study tainment, sports, by first aid responders and in many other outlining how this area has recently evolved, which provides areas. a broad and objective view of the main topics addressed by Despite the number of solutions and their different applica- scientists, is lacking. The systematic review of literature presented tions, wearables on the market sometimes differ from those in this paper investigates recent trends in wearable computing developed in academic studies. It is common to establish studies, taking into account a set of constraints applied to relevant studies over a window of ten years. The extracted articles were a connection between these two contexts once the market considered as a means to extract valuable information, creating dictates the path in which investigations should go, and vice- a useful data set to represent the current status. Results of this versa. The former frequently follows a conservative path, while study faithfully portray evolving interests in wearable devices. scientists focus on the freedom to create and try out new The analysis conducted here involving studies made over the concepts and designs. These two approaches allow the ex- past ten years allows evaluation of the areas, research focus, and technologies that are currently at the forefront of wearable ploration of new solutions through multidisciplinary concepts, device development. Conclusions presented in this review aim expanding the horizon of applications and solutions. to assist scientists to better perceive recent demand trends and However, when considering only academic studies, there is how wearable technology can further evolve. Finally, this study a lack of papers revising and understanding the research’s gen- should assist in outlining the next steps in current and future eral trends and impacts on wearable computing. Developing development. a precise scenario of this area is essential to map the way it evolves, as well as to determine the next steps. Reviews I.INTRODUCTION focused on wearables specific to various sub-areas have been During recent years, the advent of new technologies has presented [9][10][11], but they do not cover a broad area. increased the demand for wearable devices [1]. Wearables cur- There are also papers providing a more general view regarding rently vary from simple biometric signal monitoring products wearables [12][13], but do not focus on recent research trends, to a complex set of solutions. These devices can integrate with and thus contribute little to understanding and quantifying other personal electronics, providing real-time data gathering, current and future efforts to further develop this area. monitoring, and entertainment. Smartwatches, fitness bands, This article uses a Systematic Review of the Literature and AR/VR glasses are some examples of products frequently (SRL) approach to extract a set of data regarding recent found in physical and online stores [2]. research on wearable computing. Such methodology provides arXiv:2011.13801v1 [cs.HC] 27 Nov 2020 Although the wearable computing concept is not new [3][4], a formal protocol by applying a series of well-defined and this area has recently received more attention, mainly because reproducible steps, allowing it to be audited by independent of hardware evolutions. Ongoing electronic components minia- peers. This paper aims to provide a general and broad view turization has contributed to reducing devices’ size and encap- regarding the trends on wearable computing in academic sulation, allowing the design and creation of new products, studies, discussing the results of an SRL, and identifying the and embracing unexplored areas. Devices have become more current state of the art. The primary intent is to provide a wearable and pervasive, have been gaining increasing attention general view of what is being investigated regarding research over the past years, and represent a promising prospect for the focus, applicability, functionalities, hardware components, and future [2][5]. connectivity. The big picture presented by this work also Wearables are helping end-users to reach a true pervasive helps to understand the current scenario on wearables research, and context-aware environment at reduced costs [6]. Their expanding the actual investigation focus and possibly indi- effectiveness to sense users’ body signals and the environment cating the next steps in wearables evolution. In this manner, has been turned these devices into must-have products within the authors surveyed a relevant set of papers that describe certain contexts [7]. Medical and healthcare wearable devices wearables solutions spanning the past ten years. The following points summarize this paper’s most relevant contributions: From HMDs and initial prototypes until today, there has been • A broad review of wearable computing studies and their a ubiquitous set of devices applied to different contexts, e.g., applicability; vests, glasses, bands, smartwatches, medical patches, and so • A comprehensive and systematic classification of wear- on. ables solutions based on a set of constraints; Currently, wearables have reached a level of sophisticated • Identification of main research focus addressed by appli- solutions that can embed simple sensors and visually complex cation area as well as the most relevant technologies used components and algorithms. Given this scenario, current and by the devices; future research may need to explore how the devices can • A general analysis regarding the nature of published work further evolve. For this reason, a more in-depth categorization considering wearable devices. of novel solutions can help to clarify and identify trends that Results presented at the end of this paper point to a set of will guide the next improvements. exciting trends. The SRL presents a general view of how the B. General classification wearables-related studies are scattered around the globe and the impact each country has. In addition, results also provide Unlike existing products on the market, research on wear- relevant data of the most common application areas, problems, able computing has the freedom to propose and test different and technologies used pertaining to wearables research. It is concepts and approaches. When analyzing recent investiga- also possible to verify the most trending hardware components tions, it is possible to verify that a heterogeneous environment and networks used by scientists when proposing new wearable introducing various solutions exists. This subsection assigns solutions. different classifications to cover all types of wearables pre- The remainder of this work is divided as follows: Sec- sented in the literature. The different classes listed here are tion II provides a general view of the wearable computing further discussed in (Section V) and are used to classify and research scenario over the past ten years and provides an categorize the devices considered by this SRL. original classification of wearables based on a set of specific 1) Wearable type and body location: As presented by [12], parameters. Section III compares the results reached by related perhaps the most reasonable way to classify wearables is by studies and how they differ from the research presented considering where they are attached to the body. Given the by this SRL. Section IV presents the methodology used to number of existing studies/solutions, a more simplistic way execute the review, describing the steps as well as the related to index them is by considering the human body as three research questions. Section V outlines the main and most main parts. This classification, in addition to being simple, has important results obtained. Finally, Section VI presents the no ambiguity. Table I presents a description regarding device main conclusions and discusses further work. types and the location where they are commonly attached to the user’s body. II.WEARABLE COMPUTING RESEARCH

Wearables commonly describe electronic computing devices TABLE I that can be attached to the human body, aiming to sense spe- WEARABLE TYPEAND BODY LOCATION cific variables or help the end-user during everyday tasks. They Body location Description may vary from glasses, watches, strips, hats, exoskeletons, Devices that are attached to the user’s head, vests, shirts, bags, independent sensors, and any other type Head such as HMDs, glasses, brain-computer in- of device that a user can wear. terfaces, helmets, hats, and headphones. Despite the increasing number of wearable models and Any wearable covering the trunk part of Trunk the body, e.g., shirts, patches, shoulder-worn types, this paper does not focus on describing each one, devices, and belts. as this task has already been accomplished by other studies Wearable accessories attached to users’ arms, hands, legs or feet, such as wrist- [13][10][14]. Instead, this article’s primary intent is to outline Limbs bands, smartwatches, jewels, and finger- the motivations and contexts in which studies on wearables worn devices. devices have been applied. For this reason, in addition to a historical overview, this section describes a more general classification of wearables, where several parameters are taken It is also valid to consider that solutions apply to two or even into account. three body parts. For instance, a whole-body vest characterizes a solution attached to the head, trunk, and limbs at the same A. Historical context time. Figure 1 presents a visual representation related to the According to the definition of wearables provided in [15], body locations where wearables can be attached. these devices have been developed since the end of the 1960’s, 2) Applicability: Another relevant way to classify wear- as one of the first HMDs (Head-mounted Displays) [3]. Since ables is by taking their applicability into account. The ap- then, there have been three different periods of wearables plicability of a wearable solution refers to the area where it development [16]. During these periods, other proposals and is applied. Although many devices on the market focus on significant results have arisen, encompassing improved design healthcare/medicine and entertainment, studies are now being [17][4], development, and evolution of the wearables field. carried out in several other different contexts. Table II presents TABLE II WEARABLEDEVICESMAINAPPLICABILITYAREAS

Applicability area Description Wearables that help to model, test, and in- 3D model- teract with 3D structures and representations ing of the real world [18]. Wearables helping sensing data or physi- Agriculture cally actuating/supporting some aspect of agriculture tasks [19]. Devices that can be worn and used as artistic Artistic apparels and interventions [20]. Civil con- Solutions that can be used to support heavy struction or light activities in civil construction [21]. Research proposing the use of wearables to ease or increase communication and infor- Communication mation sharing between two or more people Fig. 1. Possible body locations for wearable devices. [246][40]. Wearables that can be used in the context of a kitchen, helping the end-users to per- Cooking form activities such as cooking or reading a a comprehensive set of areas where fully-functional devices recipe [22]. and prototypes are employed. Devices that can help end-users during driv- Driving ing, increasing safety by focusing on the In additon to the provided list, the same device may be road [115]. classified as associated with two or more different areas. Solutions aimed to help teachers and general 3) Research focus: educators to increase the effectiveness of The applicability areas, as presented in their classes. It can include solutions used Education subsection II-B2, have a close relationship with the challenge by students to receive more contextualized addressed by each wearable. Every challenge outlined by this information or boost their attention during subsection by Tables III and IV can be associated to an classes [23]. Devices inside this applicability area are applicability area. In this manner, the related solutions can commonly developed for entertainment pur- Entertainment also be classified or categorized according to the issue they poses (games, movies, music, and so on) aim to solve. Below, is a list of the most common challenges [24]. Environment/ Wearables used to sense a user’s surround- tackled by the articles reviewed in this paper. objects and ing context. For instance, devices helping Figure 2 presents a topology proposal that associates the individual users with visual limitations or other types above two subjects providing a better way to understand how sensing of context-aware solutions [25]. Devices focused on the development or the wearables research focus can be related to each area. Its Fabric implementation of new types of wearable primary motivation is to highlight where the research focus fabrics [26]. lies. In a more detailed analysis, it is possible to check Solutions focusing on healthcare and Healthcare/ medicine areas. This encompasses devices that some applicability areas fall within different contexts Medicine aiming to sense general bio-signal data from of research. For instance, healthcare/medicine and environ- end-users [27]. Wearables used to help during maintenance ment/individual categories are the most addressed contexts Maintenance tasks [28]. among all the considered ones. This fact highlights the relevant Devices that fall within the military context, and increasing number of wearables solutions covering these Military helping users directly in the battlefield or in two areas. Other challenges, until now, have a reduced scope, other tasks [29]. Solutions that can support users during in- Navigation being applied only on specific areas, such as “Breath analysis” door or outdoor movement [30]. and “Context awareness”. Devices used to increase the security and 4) Technologies, methods, and techniques: Wearables fo- safety levels in any environment. It en- Security/Rescuecompasses solutions providing support to cused to address the previously listed challenges frequently security and rescue operations aside from take into account several different technologies, computing their use by first responders [31]. methods, or techniques. Other computing domains so far use Devices centered on sports applicability. So- Sports lutions to increase athletes’ performance or these same resources to solve different problems. However, real-time data monitoring [32]. here they have a different focus, being employed inside Wearables created with focus on textiles. Devices within this category can be used as wearables-related contexts. Technologies, methods, and tech- Textile a basis for other, more complex solutions niques described in Table V are the most common ones [33]. considered by this SRL. Although the previous list of applicability Items present above are not an exhaustive list of “all” categories is extensive, there remain other wearables without direct association to the Others technologies, methods, and techniques used by wearable de- above, e.g., wearables that focus on elec- vices. Instead, they represent the most common items used tronic devices interaction, geology, and min- by research to propose wearable solutions. Because of their ing environments [34][35]. wide scope, some may contain other relevant subsets. For TABLE III MAINTOPICSADDRESSEDBYWEARABLESRESEARCH

Research problem Description TABLE IV Research aiming to increase autism support MAINTOPICSADDRESSEDBYWEARABLESRESEARCH (CONT.) Autism ther- and provide better therapy alternatives using apy/support wearables [36]. Research problem Description Brain-related Devices that address improvement for de- Human Wearables that focus on activities recogni- diseases tection and treatment of these diseases [37]. activity tion problems, mostly using data retrieved Through breath pattern analysis it is possi- recognition/ from inertial measurement unit (IMU) sen- Breath analy- ble to identify diseases or even infer the type Activities of sors. The recognition of a set of activities sis of activity being performed by the end-user daily living can provide a map of users’ behaviors [49]. [38]. Indoor/outdoor Devices proposing different solutions for the Cognitive as- Devices that help end-users to complete localization problems of indoor/outdoor localization or sistance simple and basic daily tasks [39]. or navigation navigation [50]. Solutions aiming to provide ubiquitous de- Context Personal vices to help users by providing context- awareness energy aware information [40]. expenditure: Daily life Devices that help to identify and monitor Solutions monitoring the user’s daily life and habits [41]. aimed to Dementia Wearables that aim to improve the effec- retrieve users’ support/ tiveness of dementia support, monitoring, or daily personal monitoring/ therapy [42]. energy therapy expenditure. General Unlike other more specific problems listed This problem diseases here, this topic involves the detection pro- can be detection cess of any disease [43]. associated Devices focusing on energy efficiency im- with Energy provement in wearables through energy ex- healthcare efficiency penditure reduction, energy harvesting, or applications any other related technique [44]. aiming to Wearables addressing how to perform estimate Eye tracking resources-efficient and precise eye identifi- the number cation and tracking [45]. of calories Devices focusing on detecting a user’s expended Fall detection falling (or pre-falling) action[46]. during a day Gait [51]. Wearables solutions addressing the problem assistance/ Physiological Any wearable device aiming to retrieve and of gait detection, assistance, support, and support/ parameters analyze physiological data parameters to tracking [47]. tracking analysis infer end-users’ health [52]. Closely related to eye tracking, this topic Posture Wearables used to gather data from sensors Gaze involves wearables that focus specifically and gesture to estimate the current posture or even a tracking on gaze direction identification and tracking recognition gesture executed by the user [53]. [48]. Any device which focuses on providing Privacy privacy [54]. Devices focused on the problem of provid- Rehabilitation ing any support to a user’s rehabilitation instance, “Artificial Intelligence” may embrace a significant [55]. Remote mon- Wearables that provide any level of remote set of algorithms, models, and methods used by related studies itoring monitoring during users’ activities [56]. (Table VI): Sleep stag- Devices focusing on monitoring end-users’ 5) Connectivity & networking: From the simplest wear- ing/monitoring sleep quality [57]. Stress Solutions aimed to provide a set of ap- ables to the most complex ones, numerous solutions are using manage- proaches to monitor or manage user stress some network connection type to send or receive information. ment/monitoring [58]. Wearables that usually have their focus on Indeed, the connectivity degree of these devices dictates the User interf. / enhancing the user interface (UI) or user data range. Briefly, they can form a wearable body area User exp. experience (UX) [59]. network (WBAN) or even be integrated to outside networks Visually im- This class includes devices aiming to in- paired people sending information to a server in the cloud. Table VII lists the crease mobility of visually impaired users support/ mo- most common network types and protocols used by wearables [60]. bility included in this SRL. Despite the relevant applications listed here, there exist other less-popular problems ad- III.RELATED WORK dressed by the researches. Anorexia therapy Others [61], color vision impairment [62], gen- Research studies in wearable computing have a close rela- eral data harvesting [63], drug usage de- tionship with the wearables offered on the market. As soon tection/monitoring [64], and others could be as products’ numbers increase to indicate popularity, or if added to this list. complexity is added or identified as necessary, more interest is generated within academic circles. On the other hand, the more research performed, the greater are the number of products TABLE VII NETWORKTYPESANDPROTOCOLSUSEDBYWEARABLES TABLE V TECHNOLOGIES, METHODS, ANDTECHNIQUESCOMMONLYUSEDBY Network type / protocol WEARABLESRESEARCH Ant+. Bluetooth. Tech./ method or technique Cellular networks (3G, 4G, 5G, GPRS, GSM, General Artificial Intelligence techniques, methods, and algo- CDMA, TDMA). rithms. Near Field Communication (NFC). Augmented Reality and Virtual Reality (AR/VR). RFID. Data from Ballistocardiogram (BCG). Wi-Fi. Brain-computer Interfaces (BCI). XBee. Beacons. Zigbee. Bio-impedance. Data from Channel State Information (CSI). Crowdsourcing. Deep Brain Stimulation (DBS) methods. arriving on market shelves. This feedback loop increases the Digital Image Processing techniques. Discriminant Analysis methods. solutions on both sides, requiring companies and scientists to Dynamic Time Warping (DTW). have available a comprehensive view of the area. Electrical Impedance Tomography (EIT). Despite the relevant number of surveys on wearable com- Electrodermal Activity (EDA) classification. puting, few have focused on providing an in-depth view of Electrophoretic Displays. Face Recognition methods. the global research being carried out. This section focuses on Fast Fourier Transforms (FFT). recent work related to the research areas presented by this Graphene material. review, separating them according to comprehensive scope. Haptic technology and applications. Heart Rate Variability (HRV). A. General research Histogram of Oriented Gradients (HOG). Human Hybrid Robots (HHR). Inside this class fall the general focus related studies cov- Internet of Things (IoT). Least Mean Square (LMS) algorithm. ering wearable computing/technology. Markov Chains. In the paper presented by [13], a classification and survey Motion History Images (MHI). are presented, with the authors describing commercially avail- Object Recognition (using SIFT/MOPED algorithms). able products and research prototype solutions. An excellent Optical Character Recognition (OCR). Data from Photoplethysmography (PPG). approach is taken, focusing on several aspects that influence Data from Root Mean Square (RMS). wearables contexts, such as the current state of the art and Seismocardiography (SCG). the solutions classification of “Accessories”, “E-Textiles,” and Solar Energy to power devices. “E-Patches” within the groups of “Existing Products” and Wearable Power Assist Leg (WPAL). Others. “Research prototypes”. The work also focuses on providing an SRL regarding communication security issues and challenges in energy efficiency, while always considering the wearables environment. Thus, the work is closely related to the one presented by this paper, and they may be considered as com- plementary, showing initial concepts, general trends, and the TABLE VI current status regarding the wearables area, but with different MAIN ARTIFICIAL INTELLIGENCE TOPICS IN WEARABLE RESEARCH focus. Algorithm, method, or model In [66] a general review on wearables is provided, contextu- Bayes nets and Dynamic Bayesian Networks alizing them within applicability domains. Emphasis is placed (DBN). on the security, privacy, and usability subparts, describing Convolutional Neural Networks (CNN). Decision Trees. possible threats, risks, and defenses of this type of device. Haar Cascade Classifiers. The work exhibits good coverage and organization. However, Hidden Markov Models (HMM). the paper is centered only on security and privacy questions. K-Means algorithms. The survey presented in [12] focuses on research prototypes Logistic Regression algorithms. Logit Boost algorithm. and industrial solutions on the market. A historical survey is General Machine Learning meth- proposed as a way to understand how the area is evolving and ods/algorithms. what the current focus is on wearable devices. The review Nearest-neighbor approaches (including k- NN). presents a solutions categorization according to the place Random Forest algorithm. where the device is attached to the users’ body. Furthermore, Supporting Vector Machine (SVM). the work compares historical trending data with the current Others. tendencies. In comparison with our work, it lacks several types of information. As can be verified from the results section (Section V), it is possible to contextualize the wearables Fig. 2. Wearables problems contextualized into each applicability area. trending data not only according to the applicability area and IV. WORK METHODOLOGY body location but also by considering a set of other parameters An SRL is a way to recognize, compare, and evaluate that will help to better clarify the research directions. relevant research in a particular area or context. Data raised by In [14] the authors present another survey, classifying an SRL allows inferring trends and patterns using an auditable wearable devices according to the scope where they are ap- and trustable methodology [71]. The SRL presented in this plied: “Assistive”, “Workplace”, “Healthcare”, and “Consumer paper follows the guidelines proposed by [71], dividing the Products”. Despite its vast scope, the work mainly focuses work into three main modules: “Planning”, “Execution”, and on devices already on the market, with a limited number “Summarization”. During the development, an auxiliary tool of products being analyzed. When compared to the work called “State of the Art through Systematic Review” (StArt) described here, it is possible to verify that a different approach [72] was used. Figure 3 details the initial actions and inner was given when analyzing wearable devices by prioritizing the tasks taken for each module. research area. In addition, this research has used an increased amount of research studies as a means to increase the results’ confidence level. B. Specific research Unlike the previous subsection, section centers on reviews with specific focus within the wearables field. Instead of providing a more general classification, related work described below focuses on reviewing distinct subparts or specific com- ponents that integrate with the wearable computing area. Within wearable computing, there are several different ap- proaches. One is the study of how to sense human body characteristics aiming to discover user behavior and activities. In [67] the authors reviewed emerging technologies used to sense users’ activities and physiological variables. It presented a set of devices dedicated to retrieve information from different areas of the human body, such as glasses, heart rate sensors, blood pressure sensors, and so on. In general, the work focuses on healthcare. Conversely, in [68] the authors also present a review focused on wearable sensing, concentrating on activity classification. Different sensors are discussed as well as research covering single and hybrid sensing modalities. The work described here has a broader scope, encompassing Fig. 3. Flowchart showing the inner tasks executed by the three main modules. both wearable sensing and activity classification while also including other areas. A. Planning A survey on wearable medical sensor systems is presented During this phase, the SRL protocol was designed focusing by [69], describing the application, typical architecture, and on its main objective: “identify and categorize recent proposals components that are used to build these systems. A sys- of wearable computing devices with their applications, appli- tematic classification is described, dividing the devices into cability areas, and technologies available in the literature”. “Healthcare”, “Human-computer interaction (HCI)”, “Security To fulfill this objective, two main research questions were and forensics”, and “Education” areas. Notwithstanding, as proposed: the scope of the work is limited, it lacks more extensive coverage that may include additional wearables applications 1) Main Research Question (RQ1): What are the main and problems, as done by the research presented in this SRL. areas in which recent wearable computing device studies In [70] a survey regarding wearable biometric recognition have been applied? systems is presented. Unlike the previous studies, this paper • Motivation: Understand the context and applicabil- touches upon an even more specific subject inside wearable ity areas where wearable research studies have been computing: biometrics. Information presented by the article carried out. This information will allow scientists to helps to define a biometrics taxonomy using wearable sensors. target their current and future works better; The authors also present concepts of signal processing tech- • Expected results: Produce a measurable conclusion niques, data classification, and machine learning algorithms. regarding the top trending application areas for Although very well executed, the survey presents a more wearable computing devices. specific range than the results described by this SRL, which 2) Secondary Research Question (RQ2): What types of aims to understand how the entire wearable devices area has features are mostly used by wearable computing device evolved over the past years. studies? TABLE VIII research will have a more significant impact when WEARABLES-RELATED KEYWORDS USED TO SEARCH ARTICLE written in this language; REPOSITORIES – Studies presenting any novel wearable device de- Search term scribing its real-world applicability and motivation ARTICLE TITLE(wearable*) AND problem; and ARTICLE ABSTRACT(activity OR augment* – Articles describing any novel wearable device and OR eye OR tracking OR gesture OR haptic OR head-mounted OR smartwatch OR virtual its internal components, technologies, methods, or OR wrist-worn OR intelligent OR finger OR hardware components. pervasive OR embedded OR hardware OR software OR medical OR camera OR recognition Given the number of retrieved articles from the “literature OR application OR applicability OR context OR search” stage, a set of exclusion rules were also crafted. Then, helmet OR glasses OR display OR electronic OR sensor OR motion OR mobile OR hand OR any article that met at least one of these rules was removed cloth* OR smart OR personal OR screen) from the SRL.

• Exclusion criteria: – Articles without an explicit title or author were • Motivation: Discover the trending topic technologies removed; currently being considered to craft wearable devices – Papers without the word “wearable*” in the title investigations. were not considered. Although this restriction was • Expected results: Provide a clear separation of wear- already considered by the keyword search, some able computing solutions so that it will be possible digital repositories were apparently unable to comply to categorize them according to the considered fea- with this constraint; tures, technologies, and methods being used in the – Studies in any language other than English were academic environment when researching wearables eliminated; subjects. – Opinions, reports, talks, and other non-scientific 1) Source selection and literature search: Through an ini- studies were also not considered; tial search of potentially relevant sources, the authors have – Short-papers, demos, abstract-only articles, com- identified a set of repositories indexing articles related to ments, and other works with less than three pages the wearable environment. The following electronic databases were removed from the SRL. Such decision was were taken into account: ACM Digital Library1, IEEE Xplore taken because of the reduced amount of information Digital Library2, Springer Link3, Science Direct4, Scopus5, available from these papers; and Engineering Village6. Articles written in the English – Proceedings documents were removed as they index language were identified within these repositories using the articles already listed by the digital repositories in- arrangement of wearables-related keywords presented in Table dependently; VIII. – Systematic reviews, reviews, and surveys were ex- This keywords-based search was applied to each electronic cluded. As in the proceedings, this type of study database, retrieving articles published between 2008/Jan/01 commonly indexes other research. Thus, removing and 2017/April/19. This time interval was considered by the then avoids analyzing duplicated work; authors as being sufficient to provide a high-quality view of the – Retracted articles; recent research being carried out in the wearable computing – Studies not available in electronic format were not devices field. analyzed. Although being returned by the digital 2) Inclusion and exclusion criteria: As soon as the studies repositories searches, some articles are no longer were gathered from the literature search, a set of inclu- available in an electronic format; sion/exclusion rules were applied to them as a means to – Articles with some digital access restrictions (i.e., refine the number and increase the quality of obtained articles. that could not be digitally retrieved) were not con- Initially, a study was considered suitable for this SRL if it met sidered; the following inclusion criteria: – Papers whose titles did not describe a wearable • Inclusion criteria: device/solution, such as antennas, data analysis, stan- – Full articles published between 2008/Jan and dard specifications, and so on; 2017/April/19 in English. The language restriction – Articles in which the Abstract did not mention was applied because the authors understand that the a wearable device/solution being proposed or de- scribed; 1https://dl.acm.org/ – All studies with two or fewer pages. As is common 2 http://ieeexplore.ieee.org/Xplore/home.jsp with most demonstrations and posters, papers with 3https://link.springer.com/ 4https://www.sciencedirect.com/ two or fewer pages commonly do not have enough 5https://www.scopus.com/home.uri room to properly describe the work methodology or 6https://www.engineeringvillage.com/home.url provide a full results section covering a wearable device, its internal components, and applicability; – Works that did not describe their methodology or results were not provided; and – Out of context studies. Papers focusing on aspects other than those considered by this SRL were dis- carded. B. Execution In this step, data raised by the two previous steps were used to search for specific and high-quality studies that precisely fit the SRL parameters. Once the sources were selected, the queries performed, and the inclusion/exclusion criteria defined, it was necessary to select which works were better qualified to be considered for this review. 1) Articles selection: After the literature search presented in subsection IV-A1, all the retrieved papers were subjected to the previously described inclusion/exclusion criteria. Owing to the number of results, the procedure was divided into five steps, as shown in Table IX. Figure 4 presents a detailed flowchart of what actions were taken in each step. From an initial collection of 18,539 articles, 3,315 articles were ultimately selected. 2) Data extraction: After the literature search and articles selection (using defined inclusion/exclusion criteria), data were extracted from the papers. The extraction form used by this review looks for the information presented in Table X, where the first column outlines the data type and the second column the possible values. A short description of what it means is also provided. Owing to the large number of studies, the data extraction step was divided into two phases: • Phase 1: Approximately 10% (338) of the studies were manually analyzed. Introduction, Conclusion and Methodology sections were read, while the remaining text was screened to extract the data presented in Table X. For each type of data being extracted, a set of keywords was also collected. These keywords were further used in the next step to automatically retrieve the remaining pa- pers’ (2,977) contents using a specially-developed mining script. Fig. 4. Flowchart describing the steps taken during article selection. • Phase 2: Using a specific script and a set of keywords collected during the previous phase, the data listed in understand the trending topics of each part. Section V presents Table X (except those in the highlighted rows) were a deep analysis of each resulting data and presents them with automatically mined and extracted from each of the appropriate contextualization. remaining 2,977 articles. This phase was necessary owing to the large number of articles to be analyzed after the V. RESULTS AND ANALYSIS filter applied in Step 6. After the previously described procedures performed in the C. Summarization “Execution” and “Summarization” parts, the resulting data After the literature search and articles selection, data was consists of information from 3,315 valid articles. Results extracted from each of the 3,315 papers using the predefined presented in this section analyze the summarized information, form presented in Table X. The resulting information was separating it into two main groups (Group 1 and Group 2) automatically exported to a spreadsheet using the support pro- according to the nature of the information. While Group 1 vided by the StArt tool. Data inside the exported spreadsheet focuses on presenting information related to the nature of the was then separately analyzed using specially built scripts. applications and their characteristics, Group 2 is a compilation These scripts isolate each information type and correlate them of results considering data extracted concerning the proposed to the work publication year, providing significant results to wearables. TABLE IX • Work in Progress (WP): Articles that describe a work STEPS TAKEN DURING ARTICLE SELECTION in progress, i.e., part of, or a module that will compose a complete solution; Step Title Description • Case Study (CS): Papers that focus on a case study; Search in the digital repositories looking for • Validity (VL): Works that aim to demonstrate the validity Step 1: Literature search articles that meet the constraints defined in of a hypothesis, theory, or physical component/prototype subsection IV-A1. Automatic verification and removal of du- through a valid experiment; Step 2: Duplicates removal plicated papers using StArt tool. • Interview (IV): Articles focusing on data obtained from Manual verification and removal of du- interviews with possible users or specific interest groups; plicated articles. Additionally, papers titles Step 3: Studies title analy- were verified looking for: studies without a • Initial Study (IS): Papers describing an initial study, sis (part 1) title, works not written in English language, which is finished but does not provide a complete so- papers whose title does not contain the lution. “wearable*” word, and incomplete studies. Second round of paper title analysis. This time studies fitting at least one of the follow- Step 4: Studies title analy- ing exclusion criteria were removed from sis (part 2) the SRL: Non-scientific studies, works that do not present a wearable device/solution, proceedings, and articles without author(s). Verification of studies Abstract information. Works whose Abstract fit at least one of the following exclusion criteria were re- moved: Incomplete studies, works not writ- ten in English, proceedings studies, articles Step 5: Studies Abstract not available in electronic format, literature analysis reviews or surveys, non-scientific studies, retracted papers, works with some digital access restrictions, and articles whose Ab- stracts clearly do not mention a wearable device/solution. This step searched for specific informa- tion throughout the paper, mainly check- ing the Introduction section (or its equiv- alent), Methodology, and Results, removing Step 6: Papers full-text ver- articles that lacked some of these parts. ification Moreover, for works addressing literature reviews and surveys, papers with two or Fig. 5. Manually reviewed works characterized according to the study type. fewer pages, works not written in English, and out of context studies were removed. Figure 5 shows a visual representation of how the articles can be categorized. Most wearables solutions presented in the papers can be classified as “Finished Works (FW)”, as A. Group 1 they have a closed scope and present a functional proto- Results presented here cover only manually extracted data, type. Other interesting data relate to the “Validity (VL)” highlighted in Table X, from 338 randomly chosen articles. studies: A reasonable amount of studies use this approach Initially, data compiled here is only concerned about the to evaluate users’ perceptions or to validate an idea. “Case study’s nature describe the general characteristics of research Study (CS)”, “Work in Progress (WP)”, and “Initial Study papers published in the wearables area. The authors took this (IS)” papers represent 6.21% of the studies, indicating that decision as a way to ease the reader’s understanding of this they have low relevance overall. This result may suggest that SRL, as due to their nature, this type of data is particularly scientists focus on publishing finished works rather than dis- difficult to be automatically extracted from articles. Further- closing tentative conclusions. Another hypothesis is that few more, this initial data also provided a significant set of samples journals/conferences in the area accept “under development” regarding the analyzed articles and the wearable prototypes articles. Notwithstanding, this theory must be confirmed with a they propose. deeper analysis. Finally, only 0.59% of articles were classified Table XI presents all the 338 papers’ manually extracted as “Interview (IV)”, demonstrating a low scientific motivation data. Resulting information was then grouped into five differ- for publishing this type of study. ent sets following the organization outlined by the highlighted 2) Does the work focus on a wearable device: This ques- rows of Table X and is further explained below. tion is asked to determine whether the wearable device de- 1) Study type: Articles were initially classified according scribed in the article is the research’s main objective/result. to the study type, considering the following possible types: This question was raised as a way to describe the real • Finished Works (FW): Works whose consistent results objectives when assessing wearable computing studies: Are are presented following a methodology and address reach they focused on building wearable devices and/or are they used objectives inside the listed scope; as a technical solution (or part of one) in other problems/areas? TABLE X SUMMARY OF DATA EXTRACTED FROM SELECTED ARTICLES

Data title Description / Possible values Title, author(s), affiliation(s), affiliation country(ies), publication year, and publication vehicle (journal, conference, book, Basic article metadata chapter, ...) Studies were classified according to their type among the following options: Validity study, case study, work in progress, Study type interview, initial study, finished work. Does the work focus on a Verify if (yes/no) the wearable device is the main research object, i.e., the focal point for each paper. wearable device? Does the work include a Verify if (yes/no) the article describes a wearable device prototype as a means to validate the proposition. prototype description? Measure how detailed the wearable device description is. Possible values are: (0) Wearable solution is not described; (1) Wearable device descrip- Solution device is barely cited through the text; (2) Device is mentioned in the text and presented through images; (3) tion level Wearable prototype and its implementation are described by the text and figures; and (4) Wearable prototype is deeply described, presenting the hardware used to design and develop the solution. Is the wearable prototype Check if (yes/no) the proposed solution is a fully-functional wearable device or just a part/module of a more complex a complete solution? solution, e.g., a sensor, an improvement, and so on. Extract the human body part where the wearable device is attached: Head, trunk, or limbs. A consistent description for Wearable location the scope of each part can be found in subsection II-B1. Classify the proposed solution into one of the following types: wristband, smartwatch, exoskeleton, helmet, necklace, Wearable type sub-vocal sensor, belt, glass, chest device, vest, glove, in-ear, finger-worn, shoe sensor, goggles, strip, halo, headphone, or independent sensor(s) attached to different parts of the users’ body, bag, jewel, hat, knee, and others. Contextualize the wearable device into one of the following applicability areas: 3D modeling, agriculture, artistic, civil construction, cooking; education, entertainment, environment/objects and individual sensing, fabric, healthcare/medicine, Applicability area maintenance, military, navigation, security/rescue, sports, textiles, and others. A full description of what each applicability area covers can be found in subsection II-B2 of this paper. Wearables solutions are categorized according to their research focus: autism therapy/support, brain-related diseases, breath analysis, cognitive assistance, context awareness, daily life monitoring, dementia support/monitoring/therapy, general dis- eases detection; energy efficiency; eye tracking; fall detection; gait assistance/support/tracking, gaze tracking, human activity Research Focus recognition/activities of daily living, indoor/outdoor localization or navigation, personal energy expenditure, physiological parameters analysis, posture and gesture recognition, privacy, rehabilitation, remote monitoring, sleep staging/monitoring, stress management/monitoring; user interface / user experience, visually impaired people support/mobility, and others. A more detailed description regarding each problem can be found in subsection II-B3 of this article. Devices were associated and classified according to the technology, method, or technique using them. The following possible values were considered according to the item relevance: General Artificial Intelligence techniques, methods and algorithms, Augmented Reality and Virtual Reality (AR/VR), data from ballistocardiogram (BCG), brain-computer interfaces (BCI), Beacons, bio-impedance, data from channel state information (CSI), crowdsourcing, deep brain stimulation (DBS) methods, digital image processing techniques, discriminant analysis methods, dynamic time warping (DTW), electrical impedance Technologies, methods tomography (EIT), electrodermal activity (EDA) classification, electrophoretic displays, face recognition methods, fast and techniques Fourier transform (FFT), graphene material, haptic technology, heart rate variability (HRV), histogram of oriented gradients (HOG), human hybrid robots (HHR), internet of things (IoT), least mean square (LMS) algorithm, Markov chains, motion history images (MHI), objects recognition (using SIFT/MOPED algorithms), optical character recognition (OCR), data from photoplethysmography (PPG), data from root mean square (RMS), seismocardiography (SCG), solar energy, wearable power assist leg (WPAL), and others. Extract connectivity and network-related information from each reviewed work. As presented in subsection II-B5, the Connectivity & network- possible values are: ANT+, Bluetooth, cellular networks (3G, 4G, 5G, GPRS, GSM, CDMA, TDMA), NFC, RFID, Wi-Fi, ing XBee, and ZigBee. As presented by [65], wearables can be categorized into five different layers according to their complexity level (Layer 0 Associated layer being the simplest and Layer 4 considering the most complex). Most common hardware components used to craft wearables prototypes and solutions were retrieved from the selected studies. Some examples are: accelerometer sensors, analog to digital converters, antennas, arduinos, cameras, ECG sensors, Hardware components EEG sensors, electrodes, Google Glass, global positioning system (GPS), gyroscope sensors, heart rate sensors, impedance analyzers, magnetic sensors, microcontrollers, microphones, printed circuit boards (PCB), PPG sensors, pressure sensors, and others.

The Figure 6 results indicate that the majority of considered or “one of the principal” results produced by the paper. articles (242 of 338) aimed to provide a wearable solution at As presented in Figure 7, 316 of the analyzed papers the end. In contrast, 96 articles made use of a wearable device described a wearable device prototype with some level of as a component of a more significant or complex solution. detail. Conversely, only 22 papers did not present a final 3) Does the work provide a prototype description: Another prototype description. As a general view of the wearable question raised was whether the analyzed studies provide a computing research context, this information infers that most device prototype description or not. This fact is particularly scientists see the creation of real prototypes as a reasonable interesting, as papers providing only the general device de- way to present their results. scription could be present. The authors considered as a prototype any device with a minimum description presented, aside from being the “main” TABLE XI: Classification based on data extracted from the articles (2008 – Apr/2017)

Wearable Work focuses Wearable de- Study Prototype de- device Reference on wearable vice complete type scription? description devices? solution? level [73] [74] [62] [75] FW – 2 – [76] [77] [78] FW 2 – [79] [80] [81] [57] [24] [53] [82] [83] FW 3 [84] [85] [30] [43] [86] [87] [88] [89] [90] [91] [92] [93] [94] [95] [96] [97] [98] [99] [100] [101] [102] [103] [104] [21] [105] [106] [107] [108] [28] [109] [110] [111] [112] [113] [114] [115] WP 1 [116] [117] [118] FW – 1 – [39] CS – – 2 [119] [120] [33] [121] VL 4 [59] [32] [122] [123] [124] VL – – 0 – [125] [64] FW – – 1 [49] [126] [127] [128] [129] [130] FW – 1 [131] [132] [133] [134] [135] [136] [137] [138] [139] [34] [140] [141] [142] [143] [60] [144] FW 1 [145] [146] [147] [148] [149] [150] [151] [152] [153] [154] [40] [155] [25] [50] FW 2 [156] [157] [20] [158] [46] [159] [160] [161] [55] [162] [163] [164] [165] [166] [167] [168] [169] [170] [171] [54] [172] [48] [173] [174] [175] [176] [35] [177] [178] [179] [180] [181] [182] [44] [183] [184] [185] [186] [187] [188] [189] [190] [191] [192] [193] [194] [195] [196] [197] [198] [41] [199] [200] [201] [202] [51] [203] [204] [205] [206] [207] [208] [209] [210] [211] [212] [213] [214] [215] [216] [217] [218] [37] [219] [220] [221] [222] [223] [224] CS 2 [225] [226] [227] [228] [229] [230] FW 4 [231] [232] [233] [234] [235] [236] [19] [237] [238] [56] [239] [240] [241] [242] [243] [244] [245] [58] WP 2 [246] [247] CS 2 [248] [249] [250] FW – – 0 – [251] VL – 2 – [31] IV 2 [252] [253] CS – – 0 – [254] [255] [256] [257] [258] [259] VL 2 [36] [260] [261] [262] [47] [263] [264] [265] [266] [267] [268] [269] [270] [271] [272] [273] [274] [23] CS – – 0 [275] WP 4 [276] [63] WP 3 [277] [278] [279] [280] [281] [282] FW – 2 [283] [284] [285] [286] [45] [287] [288] [289] [290] [291] [38] [292] [293] [294] [295] [61] [296] [297] [298] [299] [300] [301] VL 3 [302] [303] [304] [305] [306] [307] [308] [309] [310] [52] FW – 0 – [311] FW – 3 – [312] WP – 0 [313] [314] [315] [316] [27] [317] VL – 2 [318] [319] [320] [321] [322] FW – 0 [323] [324] FW 0 [325] VL – 0 – [326] CS 3 [327] VL 0 [328] VL – – 0 [329] [22] [330] [331] [332] [333] [42] VL – 1 [334] [18] [335] FW – – 0 [336] VL – – 2 [337] IS 1 [338] CS 1 [339] [340] [341] [342] [343] VL 1 [344] VL – 0 [345] FW – 0 [346] [347] [348] [349] [350] FW – 3 [351] [352] VL 1 – [353] IV – 2 [354] WP – 2 [355] [356] IS – 2 [357] [358] FW 4 – [359] FW – 4 [360] FW 1 – [361] [29] FW 3 – [362] FW 0 – [363] IS 4 [364] VL – 1 – Fig. 6. Number of studies in which the wearable device is the main research output.

Fig. 8. Wearable device prototypes description levels.

5) Is the wearable prototype a complete solution: The final question to be answered by this SRL concerns the scope of proposed wearable devices. Many articles include research that can be used in other research, or even by more general solutions involving embedded computing rather than wear- able devices. Components such as sensors and vests/textiles technologies can apply to other solutions in different areas of research. Fig. 7. Verification of whether the analyzed studies present a wearable device prototype.

4) Wearable device description level: If there is a prototype described by the work, the question is how well this device is presented in the paper. Table X shows five different degrees detailing the produced device according to how deep is its description: • 0: Prototype is not described by the paper; • 1: Wearable device proposed by the article is lightly mentioned throughout the text; • 2: Prototype is mentioned in the text and demonstrated through the use of images; • 3: Device and its implementation process are described in the text and presented using images; Fig. 9. Verification of whether the considered prototypes are a complete wearable solution. • 4: Wearable device is deeply described by the text, with most (or all) of its hardware components being presented, Data extracted from the papers can be used to create the as well as the design and build process. chart presented in Figure 9. Results show a massive number of Analyzing the data presented in Figure 8, it is possible works describing complete prototypes, usually contextualized to verify that the majority of papers provide a superficial inside an area trying to solve a specific or general problem, description of the resulting device (Level 2: 161 or 47.60%), as demonstrated through the analysis in Section V-B. while a more profound explanation (Level 4) is found in only The data presented here is also remarkable when taking 9.17% of the articles. This result may suggest that scientists into account the data already discussed in previous sections. only describe the necessary parts so that readers can gain a Together this information can provide useful view regarding basic understanding of their work, a fact also corroborated the nature of works being published in the wearable computing by the percentage of papers that do not provide a minimum field. Through a superficial analysis, it is possible to affirm description of the resulting device (Level 0: 25 or 7.40%). that scientists are focusing on complete prototypes, which commonly are presented at a reasonable completion level in Figure 10 presents a graphical separation and distribution of their papers. wearable devices studies over the past ten years, as considered The next section provides a more in-depth discussion of the by this review. It is possible to see that the number of studies in main areas, problems, and general nature of studies within the 2016 is more than four times the number in 2008. This reflects context considered in this article. the increasing interest in wearables investigations, probably carried out in response to the demands generated by the market B. Group 2 (and vice-versa). The information presented here covers all the non- The number of published papers in 2017 corroborates this highlighted data listed in Table X. Results presented for this growth trend. In almost four months of analysis, more articles group cover all articles (3,315). Except for the articles where were published than during 2008, 2009, and 2010 combined. data was manually extracted (338), all remaining papers were If this trend is maintained, 2017 will have almost as many (or automatically processed by mining scripts. even more) studies published than in 2016. The results described here were also arranged according to The facts raised above highlight the importance of wearable the extracted data to which they refer. A set of specific charts devices in the research context. A more systematic view of this and tables eases the task of comparing the items within each scenario can be found in Table XII, which shows the variation topic. Providing numerical values and visually comparable percentage year by year. Although the data regarding 2017 is bars to correlate results inside each topic display a weighted not entirely presented, it tends to have numbers in the same visual representation of the associated numerical values. range as 2016 if the same pace of first quarter was maintained 1) General number of studies: As previously described, for the remainder of 2017. studies involving a wearable device were screened from 2008 The real impact of wearables research can be seen by to 2017. Although this review did not completely cover 2017, analyzing the last line of Table XII. It presents a value it is possible to observe an increasing number of studies in indicating an increase of at least 451.5% in less than ten years this year. Increasing numbers of articles can be noted when (disregarding the last year). grouping them by the publication year. 2) Affiliation country contribution: Through the affiliation information, the authors have extracted data for associated TABLE XII countries. This type of information may be used to build a NUMBEROFSTUDIESCOVERINGWEARABLEDEVICES (2008 – APR/2017) correlation between the interest level on wearables research Year Number of studies Variation from previous year and each country. Furthermore, as described in Section IV-B, 2008 171 – the filters implemented by this SRL allowed gathering of a set 2009 172 +0,6% of more relevant articles, providing high-quality information. 2010 192 +11.6% 2011 251 +30,7% Table XIII shows the countries that have been involved in 2012 235 -6.4% at least ten (10) studies during the considered period (2008 – 2013 312 +32,8% Apr/2017). This information allows some conjecture regarding 2014 422 +35.2% 2015 593 +40,5% the relationship that has developed (or is under development) 2016 772 +30.2% that countries have with studies on wearables. The initial 2017/Apr 195 – places of this list are mainly composed of countries under these Total/Max. 3315 +451,5% two statuses. Curiously, the sum of articles of the participation of the three first countries is higher than the sum of the next eleven (11) countries on the list. A graphical representation makes it possible to view the impact caused by the two first countries of the list in compar- ison with other participants. Figure 11 associates the number of works published by each country with their geographical position on the globe. The map shows an evident polarization of contributions by the countries of the northern hemisphere, particularly the European area and United States. However, relevant works can also be highlighted in the Oceania area (Australia, Indonesia, and so on) and in a few countries of South America. In addition to the countries listed in this table, there are other nations with participation of less than ten works: Brunei, Slovakia, Philippines, Ecuador, Peru, Costa Rica, Ukraine, Lebanon, Chile, Russia, Bangladesh, Sri Lanka, Macau, Viet- nam, Croatia, Egypt, South Africa, Serbia, Argentina, In- Fig. 10. Percentage of wearables studies, separated by year. donesia, Luxembourg, Latvia, Cyprus, Saudi Arabia, Qatar, TABLE XIII of wearables developed to be worn on “Limbs” in comparison WEARABLE DEVICES STUDIES CATEGORIZED BY AFFILIATION COUNTRY to the other two regions. It is also valid to highlight that, in (2008 – APR/2017) every year there were a relevant number of works that did Country Contribution (# of works) not describe where the device possibly fits on the body. As USA 869 in previous subsections, this chart also shows a similar curve Japan 757 of evolution following the number of articles published in the China 344 Italy 292 considered period. South Korea 246 Given the most common places where a wearable can be United Kingdom 226 attached, a more detailed investigation can characterize the Australia 224 Taiwan 164 type of device and more specific places where devices are Canada 163 worn. Table XIV shows a relationship between the wearable Germany 163 type/“location place” with the number of studies in which they Switzerland 130 India 121 appear. Data presented in the second column counts the num- Spain 81 ber of articles that index devices of the type listed by the first Hong Kong 69 column. This type of characterization is particularly difficult, Singapore 61 Netherlands 53 as there is no general classification pattern applicable to all Portugal 49 studies, resulting in some discrepancies and redundancies. This Ireland 48 is the main reason why the sum of all studies exceeds the France 45 Belgium 44 total number of papers analyzed by this SRL. However, these Finland 38 repetitions cannot be ignored because they present essential Romania 34 patterns and behaviors. Sweden 34 Greece 33 Initially, body locations with a high number of referring Malaysia 28 articles are the general ones: “Arm-worn”, “Wrist-worn”, Brazil 23 “Chest-worn”, and so on. This scenario occurs because some New Zealand 23 Slovenia 20 papers do not precisely specify the location where a device can Poland 19 be worn. Moreover, the two first most-cited positions (“Arm- Turkey 19 worn” and “Wrist-worn”) suggest a significant use of devices Mexico 17 Thailand 16 such as watches, bands, and wrist strips. Corroborating the Denmark 15 data presented in Figure 12, the latest positions in Table XIV Norway 13 are filled with solutions worn in the head region, such as “In- Israel 10 Austria 10 ear”, “Hat”, “Headphone”, and so on. A trending analysis of data as presented by Table XIV is shown in Figure 13 through the information discretization considering the past years. The related chart yields a set of Colombia, Northern Ireland, Pakistan, Czech Republic, United some interesting conclusions: Arab Emirates, Hungary, and Iran. • “Arm-worn” and “Wrist-worn” research solutions are 3) Wearable type and on-body location: The extracted data currently the most popular ones. However, between makes it possible to categorize the articles according to the 2011–2013 there was an increasing interest in devices device type and on-body location where the user wears it. attached to the user’s chest (“Chest-worn”); Initially, considering the analyzed articles, three major body • Devices attached to the user’s leg (“Leg-worn”) were not areas were identified: Head, trunk, and limbs. This SRL popular in 2008. On the other hand, this type of device associates every work to one of these three parts, highlighting indicates growing interest in research in recent years, the most common locations where devices are worn. In the reaching the fourth ranking in 2016 and 2017; second part of this subsection, a broader classification is • A more incisive evolution can be observed when taking presented, taking into account devices models and types. “Glasses” into account. From a reduced number of ref- Figure 12 summarizes the most common locations where a erences in 2008, these devices have significantly evolved wearable device is attached to a human body. Data presented during later years. A possible reason for this behavior by this figure consider the location where the user can wear a may be associated with the “boom” of Augmented Reality solution. Note that the sum of percentages will be higher than (AR) and Virtual Reality (VR) devices that began in 2012. 100%, as the same device/solution can be worn in different This is confirmed by the data presented in Figure 16. body locations. Resulting data indicates that the “Limbs” As with previous results, the chart described in Figure 13 (70.8%) are the most common location to attach a wearable, also presents the same curve tendency, reflecting the increasing followed by the “Trunk” (53.8%) and finally the “Head” number of studies conducted during the latest years. (24.6%). The chart on the right shows how the devices’ 4) Applicability areas: Subsection II-B2 has presented the wearability has evolved over the last ten years. From 2014 applicability relevance of various wearable devices. This sub- onward, there is a relevant discrepancy regarding the number section shows the results regarding information extracted by Fig. 11. Number of published studies based on country.

Fig. 12. Distribution of wearable devices location on a human body. Fig. 13. Most common device types and locations to attach a wearable device according to the research published during the considered period. considering applicability. This type of data is particularly declined in interest, such as “Textile” and “Fabric”. Mention important to indicate past, current, and future trends. of “Education”, “Security/rescue”, and “Navigation” areas is Data presented in this subsection was grouped considering warranted, as they have maintained the same growth rhythm only the most common applicability areas. It is also valid over the years. to note that the same published article may appear in two Finally, when analyzing just 2017, it is fairly obvious that or more applicability areas owing to its scope. Table XV the same tendencies described above are maintained, although presents an overview of applicability area popularity within in a reduced number, as just the beginning of the year was the considered window of time. Through a deeper analysis, it included in the SRL. is possible to highlight the wearables importance in healthcare 5) Research focus: As crucial as the applicability areas and medicine, as they encompass most of the studies. Because are for the problems addressed by the wearable devices, this of its nature, the second most addressed area of applicability subsection presents the most common research focus. As a (“Environm./individual and object sensing”) may be used in means to reduce the analysis scope, only the most frequent healthcare/medicine wearables, as it commonly overlaps hu- problems were listed. Presented data can be employed to man body sensing for healthcare purposes. In addition, another estimate hot-trending subjects, as well as to change the focus essential index can be obtained when comparing the sum of of scientific investigations. As reported previously, the research the studies inside the two first applicability areas, which is focus list of considered topics was manually extracted from higher than the sum of all remaining areas. a set of 338 articles used as samples. The remaining papers A graphical analysis regarding the impact of each appli- were then automatically analyzed using these 338 papers as a cability area is shown in Figure 14. Despite the most repre- baseline. sentative areas, this figure also shows the trends in wearable As listed in subsection II-B3, problems considered by this devices research during the last ten years. As previously SRL are the most common ones frequently addressed by concluded from Table XV, here it is also possible to verify the wearable research. Table XVI presents articles categorized and increasing number of papers addressing “Healthcare/medicine” indexed according to the problem they discuss. The second and “Environm./individual and object sensing”. Additionally, column shows the number of studies in which each topic the “Sports” area should also receive attention, as this topic has was listed. This data provides a representative insight regard- been involved in a significant number of studies during recent ing hot-topics/problems currently discussed in the research times. Conversely, hot topics at the end of last decade have literature. Reflecting the applicability analysis (subsection Fig. 14. Number of published studies separated into applicability areas during considered years.

V-B4), the four most addressed problems are associated with ually extracted from 338 papers randomly selected, while the healthcare/medicine applicability areas. Some of the remaining remaining articles underwent an automatic mining script. To topics can also be contextualized inside this area, although this summarize the retrieved data, only most relevant technologies, association may not be directly presented or discussed in any methods, and techniques are described below. of the considered articles. Table XVII presents a summary of the most common More relevant and in-depth analysis can be performed technologies, methods, and techniques found in the literature through Figure 15. This figure shows how the research on and proposing solutions for wearable devices. The second column interest in each problem have evolved, year by year. Although values refer to the number of papers using the related technol- the “Gait assistance/support/tracking” problem is considered ogy, method, or technique from 2008 to Apr/2017. As with by a large number of works, there is also a relevant number previous categorizations, the same work may be counted in of articles covering topics such as the detection of diseases two different categories of problems. Additionally, Table XVII or general disturbances (“Diseases/disturbances detection”). shows only the most common categories found in this review. From this figure, initially less relevant problems have gained These facts are the main reason why the sum of “Number of prominence over the years, such as the sleep quality moni- works” differs from the total number of evaluated papers. toring (“Sleep stating/monitoring”) and wearables applied to Information presented in Table XVII shows a great number general rehabilitation. of proposals making use of “Artificial Intelligence” technolo- Finally, in the bottom part of Figure 15, it is possible gies. More than 25% of all studies refer to this area. This to observe a significant number of less-addressed subjects. trend has strengthened in recent years owing to the resurgence Knowing the most relevant areas may allow scientists to of artificial intelligence, as depicted in Figure 16 between change their research focus on current or future work. Addi- 2010/2011 and 2015/2016. tionally, although the fact that the three most addressed topics Figure 16 also provides relevant insight into how the use of have not changed over the years, this review has considered technologies, methods, and techniques applied to the wear- only the relevant problems, with a set of minor problems not able devices development has evolved over the past years. being listed in Table XVI. Aside from the “Artif. Intelligence” technology, three other 6) Technologies, methods, and techniques: This type of technologies can be featured in the chart. The use of “Haptic” information allows verification of the most trending topics and “AR/VR” technologies in wearable devices has increased based on a set of different options, providing a snapshot of over the years, especially during the 2015/2017 period, where trending areas in wearable devices. As with previous analyses, their popularity surpassed the remaining items. The same data subjects presented by this subsection were initially man- behavior can be correlated to works using “Root Mean Square Fig. 15. Most common problems addressed by wearables research during considered period.

Fig. 16. Most common technologies/methods and techniques addressed by wearable research during the considered period. TABLE XIV MOSTCOMMONTYPESOFWEARABLEDEVICES (2008 – APR/2017) TABLE XVI Wearable device type/location Number of works WEARABLEDEVICESSTUDIESCATEGORIZEDACCORDINGTORESEARCH Arm-worn 682 FOCUS (2008 – APR/2017) Wrist-worn 642 Chest-worn 580 Research focus Number of works Finger-worn 475 Gait assistance/support/tracking 965 Leg-worn 466 Diseases/Disturbances detection 789 Knee-worn 325 Posture and gesture recognition 668 Glass 322 Rehabilitation 450 Belt 298 Human act. / Act. of daily living 338 Ankle-worn 277 Stress management/monitoring 311 Waist-worn 260 Fall detection 270 Shoulder-worn 243 Sleep staging/monitoring 261 Thigh-worn 183 Daily life monitoring 253 Shirt 182 User interface/user experience 251 Vest 176 Breath analysis 226 Shoes sensors 166 Cognitive assistance 171 Neck-worn 164 Energy efficiency 162 Wristband 158 Privacy 125 Exoskeleton 153 Visually impaired support 123 Glove 150 Remote monitoring 89 Patch 150 Context awareness 78 SmartWatch 127 Personal energy expenditure 78 Bag 72 Physiological parameters analysis 76 Helmet 71 Gaze tracking 65 Strip 66 Eye tracking 43 Backpack 63 Dementia support/monit./therapy 39 In-ear 62 Indoor/Outdoor localiz./navig. 35 Hat 56 Autism therapy/support 26 Headphone 53 Brain-related diseases 13 Necklace 44 Dead reckoning navigation 12 Headband 33 Goggles 27 Backbone 18 SmartBand 17 Head-worn 16 Jewel 7 Halo 3

TABLE XVII WEARABLEDEVICESSTUDIESCATEGORIZEDBYTHETECHNOLOGIES, METHODS, ANDTECHNIQUESTHEYADDRESS (2008 – APR/2017) TABLE XV WEARABLEDEVICESSTUDIESCATEGORIZEDBYAPPLICABILITYAREA Tech./method or technique Number of works (2008 – APR/2017) Artificial Intelligence 870 Haptic 380 Applicability Area Number of works AR/VR 330 Healthcare/medicine 1733 Root Mean Square 289 Environm./objects and ind. sensing 1284 Heart Rate Variability (HRV) 168 Sports 397 Phtopletysmography (PPG) 158 Entertainment 265 Fast Fourier Transform 147 Security/Rescue 243 HMD/HUD 108 Textile 236 Internet of Things (IoT) 105 Navigation 235 Brain-computer interf. (BCI) 60 Fabric 234 Beacons 52 Education 206 Object recognition 52 Civil construction 190 Dynamic Time Warping 50 Artistic 152 Graphene 42 Maintenance 98 Electrodermal Activity (EDA) 35 Military 78 Face recognition 27 Learning/Teaching 68 Least Mean Square (LMS) 27 Cooking 38 Solar Energy 25 Agriculture 26 Deep Brain Stimulation (DBS) 17 3D modeling 11 Electrical Imp. Tomography (EIT) 17 Elect. devices interaction 6 Geology 2 (RMS)”, which is a measure commonly applied to signal until 2017. For instance, the use of “Support Vector Machines” processing techniques. This information can be proven by was scarce in 2008, while the same cannot be said in 2016, analyzing the data presented in V-B4 and V-B5 subsections, with this topic occurring frequently with increasing relevance. highlighting prominent studies on related areas, such as: The same can be said when analyzing other AI/machine “Healthcare/medicine”, “Environment/individual sensing” for learning approaches, such as: “K-nearest Neighbors (KNN)”, “Applicability areas” and “Gait assistance/support/tracking”, “Logistic Regression”, and “Naive Bayes”. “Posture and gesture detection”, and “Diseases/disturbances Finally, the curve shown by the chart is very similar to those detection” for “Addressed Problems”. presented in “Applicability areas” (V-B4) and “Addressed During the initial years considered by this SRL, the numbers problems” (V-B5) subsections, suggesting that these research of studies associated with all technologies, methods, and topics have also increased at the same pace as the number of techniques were close to each other. However, this has changed studies. in recent years, where three prominent groups can be seen in the chart: 1) Artificial Intelligence, 2) Haptic devices, AR/VR devices, RMS, and 3) Remaining items. It is valid to highlight the use of the “Heart Rate Variability (HRV)” measure by wearable devices. Works published during 2008 made few references to the use of such a measure. However, this scenario has changed during the last years. Now, the HRV measure is broadly used in current investigations. One possible reason for this behavior could be related to the increasing number of fitness trackers devices (bands, stripes, and related devices), what may motivate new studies using this type of data. Owing to the high relevance presented by “Artificial Intel- ligence” in comparison with other items, this SRL a more profound investigation was taken. Through the previously ex- Fig. 17. Artif. Intelligence main topics covered by wearable devices re- tracted data, this technology was analyzed aiming to discover searches. what are the main components that contribute to its popularity, making it appear in so many studies. Table XVIII depicts the 7) Connectivity & network: Another interesting analysis main contributors to the increased use of “Artif. Intelligence” results from the connectivity information of wearable devices technology by studies in the wearable devices context. proposed in the studies. The extracted information, mainly the “Connectivity & network”, field can be used to classify most TABLE XVIII common types of networks. This information is particularly ARTIF.INTELLIGENCE -MAINTOPICSSCREENINGCONSIDERING interesting for new investigations, as it is possible to verify WEARABLEDEVICESSTUDIES (2008 – APR/2017) which types of networks are the most popular for wearables. Artif. Intelligence Topic Number of works Additionally, through this data, scientists may choose to con- Machine Learning 396 duct their research on less explored types of networks, or even Neural Networks 150 use already developed solutions. Support Vector Machines (SVM) 132 Hidden Markov Models (HMM) 127 Table XIX shows a categorization of articles reviewed by K-nearest Neighbors (KNN) 115 this SRL according to the network they used. As expected, Decision Tree 107 most devices that make use of a network connection use a Principal Comp. Analysis (PCA) 102 Naive Bayes 69 “Bluetooth” interface. This fact may be explained because this Random Forest 57 type of network frequently has low energy consumption hard- Logistic Regression 40 ware available, making such interface propitious for wearable Crowdsourcing 25 Haar Cascade Classifier 23 device use. Dynamic Bayesian Nets. (DBN) 21 Moreover, “Wi-Fi” and “Mobile networks” are presented, Convolutional Networks 15 respectively, as the second and third most common network interfaces. Currently, these network types are frequently used Additionally, Figure 17 shows a more detailed view of as hotspots for Internet access or to provide a seamless how artificial intelligence use has evolved over the last years connection over a large area, despite their constraints in energy in the wearables context. The majority of papers indexed consumption, which may impact devices’ autonomy. by this SRL make some reference to “Machine Learning” A note regarding the “ZigBee” and “XBee” entries should techniques/methods, and existing algorithms are presented by be made. “ZigBee”, although being specified under the IEEE both Figure 17 and Table XVIII. 802.15 (Bluetooth) standard, was considered here as an in- The presented chart also makes it possible to check how dependent specification. In the same manner, “XBee” is artificial intelligence used by wearables evolved from 2008 commonly the name given to a set of hardware modules manufactured by the Digi1 company. However, some solutions are typically used by hobbyists to create off-the-shelf devices presented by the articles make use of these modules as a in different applicability areas. As a means to evaluate the network interface standard. Thus, to provide a more detailed most frequently used and trending hardware, this SRL took the description of this category, the authors have chosen to present extracted data from the “Hardware components” field (Table them separately. X), and categorized the results into different levels to associate them with a problem/applicability area previously described. TABLE XIX Unlike the prior analyses, hardware data extracted from the CONNECTIVITYANDNETWORKTYPESUSEDBYWEARABLEDEVICES articles and presented in Table XX were classified according to (2008 – APR/2017) the total number of works during the period and the percentage Network type Number of works of papers referring to each component. This organization eases Bluetooth 982 the task of determining the popular hardware, and provides Wi-Fi 271 a reasonable view of top trending applications. This table Mobile networks 211 indexes a set of highly relevant hardware components retrieved ZigBee 205 RFID 159 through the “Data extraction” step, providing a measure re- XBee 63 garding the impact of each component. NFC 32 An initial analysis can be made by considering the top- Ant+ 2 trending items listed in Table XX. The “Accelerometer sensor” is listed as being the most referred-to hardware component, Figure 18 presents a discretization, considering recent years, being used by 32.46% of the research prototypes. This in- regarding the most used network interfaces. The “Y” axis rep- formation can be better understood looking back to the data resents the number of solutions using the related interface on a presented in subsections V-B4 and V-B5. There, the “Health- logarithmic basis. The background bars show the total number care/medicine”/“Environment/individual sensing” applicability of works evaluated year-by-year, allowing a comparison metric areas and “Gait assistance/support/tracking”/“Posture and ges- for the impact of each network interface. Data presented in this ture recognition” problems were listed as the most popular chart complement the information previously shown in Table ones. Particularly the second one makes intensive use of XIX, emphasizing the relevance of “Bluetooth” use over the Inertial Measurement Units (IMUs) hardware, which consist period analyzed. Furthermore, note how the “Wi-Fi” network of accelerometer, gyroscope, and magnetometer sensors. An- usage has evolved over the past years. In 2008 this network other interesting point is the relationship of these hardware was the fifth most common interface and as of 2017, it takes components with data presented in subsection V-B3, where second place. the most common locations to attach a wearable device are Another interesting fact is related to mobile network displayed. “Wrist-worn”/“Arm-worn” positions can be high- (“3G/4G/5G/CDMA/TDMA/GSM/GPRS”) popularity. Their lighted as being locations frequently used by devices that use has declined since 2013. However, such networks are now sense individual activity (gait, posture, gesture, and so on), becoming more popular for wearable devices, with their use consequently making use of IMU sensors. taking the third position of most popular ones. Moreover, 19.00% of the works use a “Camera” as one In summary, these results tend to indicate a direct as- of their components. Again, this fact can be justified when sociation between energy consumption and mobility levels. considering that many types of devices use this hardware to While “Bluetooth” solutions provide a local network with retrieve images for real-time or post-processing. “Gait assis- high mobility level, “Wi-Fi” is commonly used as a bridge tance/support/tracking”, “Gaze tracking”, and “Eye tracking” to deliver wearable device data to the cloud, but imply a low are just some examples of possible application areas where mobility degree. In the chart’s middle and bottom parts, the solutions can use a camera. Note the other top-listed hardware remaining network types vary from low (“NFC” and “ANT+”) components. Almost all can be associated with the “Health- to medium (“RFID” and “XBee”) usage. care/medicine” areas, corroborating previously presented re- 8) Hardware components: During recent years, hardware sults. components have evolved in size through their miniaturization, Figure 19 shows a chart illustrating the hardware compo- and hardware cost has also been reduced. Today, a hobbyist nents usage percentage over recent years. As previously pre- developer or scientist has access to high-quality prototyping sented, “Accelerometer” sensor and “Camera” were the most hardware at a reasonable price, a fact that has helped to referred items. More than 30% of all articles have made some increase the number of proposals and solutions in wearable reference to the former, and almost 20% of solutions used the computing. This review has considered a set of hardware latter. This behavior is apparent over all the considered years components and products used by wearables over the last with minor variations. years, helping to create a specific classification of this subject. Another impressive result is related to the prototyping plat- Wearable device prototypes proposed by current studies forms. Despite their low relevance, Table XX shows a consid- make use of known hardware components. These components erable number of works using “Arduino” and “Raspberry Pi” boards to create wearable device prototypes. As can be seen in 1https://www.digi.com/xbee Figure 19, use of these components has increasingly evolved Fig. 18. Network types used by wearable devices solutions during the considered period.

over the considered years, being employed by hobbyists and scientists within the academic context. TABLE XX It is also valid to note the advent of the Google Glass MOSTCOMMONHARDWARECOMPONENTSUSEDBYWEARABLEDEVICES device. Until 2014, few articles considered this device when (2008 – APR/2017) proposing solutions or prototypes. However, after 2014, a more Hardware component Number of works Percentage significant number of wearables using Google Glass can be Accelerometer sensor 1076 32.46% observed. With its reasonable price to end-users, this solution Camera 630 19,00% appears to be well accepted by academia as a way to accelerate ECG sensor 512 15.44% Electrodes 507 15,29% the development of wearable devices or to validate initial Gyro sensor 494 14.90% ideas. Heart rate sensor 437 13,18% Microcontroller 407 12.28% Although Table XX presents the most frequently used hard- Impedance analyzer 225 6,79% ware components, this SRL has indexed many other items with GPS module 219 6.61% low relevance. Figure 20 shows a word cloud of all the indexed A/D converter 211 6,36% Pressure sensor 192 5.79% hardware, visually showing the impact each element has in Arduino 184 5,55% comparison with all others. In addition to the information Magnetic sensor 161 4.86% already presented by the referred table, it is also possible to EEG sensor 149 4,49% Antenna 137 4,13% see the most comprehensive set of elements being used, high- Temperature sensor 123 3,71% lighting those top-listed in Table XX. Other components such PPG sensor 122 3.68% as “FPGA”, “oximeter”, “ESP8266”, “compass”, “buzzer”, Printed Circuit Board 114 3,44% Microphone 113 3.41% and “Apple watch” are present, indicating that considerable Piezoelectric comp. 112 3,38% wider spectrum of components is used by scientists to propose Google Glass 98 2.96% prototypes and solutions. DC motor 78 2,35% Vibration motor 70 2.11% A set of final analyses and conclusions can be made through Strain sensor 68 2,05% the data presented in this subsection. Third-party solutions Oximeter 50 1.51% are frequently used as a basis to compose more complex Potentiometer 49 1,48% Raspberry Pi 33 0.99% prototypes, helping to further expand the wearable computing field. Moreover, the recent popularity of prototyping platforms, such as Raspberry Pi, Arduino, Intel Edison, Intel Galileo, Texas IT Beagle Bone, and so on, have also promoted the Fig. 19. Percentage of wearable devices studies using different hardware components over the considered years.

data retrieval, and transmission to a third-party server. Fitness trackers/bands, health monitors, and independent sensors are the most common examples of these devices; • Layer 1: Devices with a simple visual display that shows 2D images (icons, circles, squares, rectangles and any other pre-processed image), and text. This layer can act to pinpoint a physical place, representing a “point-of-interest (POI)”; • Layer 2: Wearable solutions with 3D-rendering capabil- ities that possibly process user interactions in addition to the functionalities provided by lower layers. Graphics enable the device to handle AR and VR applications, Fig. 20. Word cloud presenting wearable devices’ main hardware components providing a set of more realistic applications; relevance. • Layer 3: Wearable equipment that allows graphics ma- nipulation on-screen, overlaying real-world images. This design and development of wearables over a new range of layer allows a certain level of interaction with the end- solutions. user as a means to increase user experience; 9) Associated layer: [65] proposes a novel method to • Layer 4: Devices supporting “Artificial Intelligence categorize wearable device solutions, associating them to (AI)” techniques, such as machine learning and pattern different layers according to their functionalities. This clas- recognition, to extract, match, and process real-world sification starts with the simplest possible solution (Layer 0) data/objects. The support of additional hardware may be and incrementally adds new functionalities until it reaches the required to meet real-time constraints. most complex ones (Layer 4). It is valid to note that higher Table XXI describes how wearables can be classified ac- layers can reuse features represented by lower layers. A short cording to the five layers based on the articles considered in description regarding the types of devices in each layer is this review. Given that “Layer 0” is mostly composed of simple provided. devices to sense the environment, it is reasonable to have more • Layer 0: Wearables without a visual display. Being the than 80% of all devices included in this set. closest to IoT devices, they mainly focus on user sensing, The data presented in Table XXI agree with the results presented in subsections V-B4 and V-B3. The first indicates with simple wearable devices in comparison with the most that the “Healthcare/medicine” and “Environment/individual complex solutions. This difference can partially be explained sensing” are the most popular applicability areas for wearables, by considering that wearables in “Layer 0” most of the times justifying why “Layer 0” is the most referred-to, as both areas make use of simple sensors and can be applied over a broader are composed of devices that retrieve data without the use range of solutions than any device contextualized into the other of a visual display. Moreover, data presented in subsection layers. Despite this, as the number of studies increases in other V-B3 help to corroborate these results, as “Wrist-worn”, “Arm- areas, such as AR/VR and HUD/HMD, this gap is expected worn”, and “Chest-worn” are body places frequently used by to decrease to some extent. the solutions, also being the most common positions where a device inside “Layer 0” is placed. The relevant number of solutions classified as “Layer 3” can be explained through the same approach. Considering that this layer consists of devices making use of visual interfaces overlaying graphics on the screen, such as glasses and AR/VR goggles, it is easy to understand why it is a popular level. According to the data shown in subsection V-B6, “AR/VR” technologies are widely used in wearable solutions, while “Glasses” are listed as one of the most popular types of devices in subsection V-B3. Layers 1, 2, and 4 are the intermediate points. While “Layer 1” holds elements with simple display capabilities, “Layer 2” Fig. 21. Distribution of wearables solutions according to their layers over the devices cannot be categorized as being complex solutions. In considered years. turn, “Layer 4” prototypes are more complex and are not as popular as those listed inside “Layer 3”. It is reasonable to assume that data presented here is simply a snapshot of the VI.CONCLUDING REMARKS current scenario, which may start to change at any time by Today, wearable devices on the market provide a consider- market or academic demands. able number of solutions to be used in several different areas. Solutions applied to entertainment, sports, healthcare, rescue, TABLE XXI and other areas can be currently found in stores. In turn, the WEARABLEDEVICESLAYERDISTRIBUTIONS (2008 – APR/2017) popularity of these devices challenges scientists to investigate Layer Number of works Percentage new problems and opportunities, proposing novel solutions and Layer 0 2738 82.59% specially raising new questions. Layer 1 37 1,12% Layer 2 116 3.50% A. Summary Layer 3 359 10.83% Layer 4 65 1.96% This systematic review presents a snapshot of current trend- Total 3315 100% ing topics in the wearables field over the past ten years. The methodology used by this paper allowed the authors to revise Figure 21 shows how the wearable devices association with and categorize a large set of studies focused on electronic the layers has evolved during the last ten years. The percentage wearable devices. In total, 3,315 papers were found describing of studies is presented with their related classification for each different wearable solutions. The data extracted from these year. Validating the results presented in Table XXI, the chart papers have allowed a consistent analysis, providing clues shown by this figure also reveals that most of the devices can about the nature of the work being published and providing be associated with “Layer 0”. The relevant difference here is relevant information regarding the most trending topics in this related to how the number of articles classified as pertaining to area. Based on the number of analyzed papers, the authors each layer has evolved over the past years. A possibly typical believe that the final conclusions faithfully reflect the real, case is the comparison of works percentage in “Layer 2” current scenario of this research area. versus “Layer 4”: The number of articles proposing a wearable Through the provided data, it was possible to highlight the device in each of these layers was almost the same until increasing interest in wearables technology, with a growth of 2014. From this time on, the percentage of papers addressing more than 300% in the number of published papers since the devices classified inside “Layer 2” surpassed those in “Layer beginning of the considered period. This initial conclusion 4”. These results can be associated with the advent of virtual leads to a set of new findings, which are presented below. reality glasses and their applications during recent years, which moved from scientific experiments to the consumer market, B. Main findings generating an increasing demand for studies in this context. From the analyses made throughout this review, and con- The chart allows concluding that there is a substantial sidering the scope addressed by each paper, a relevant number discrepancy between the number of studies being developed of main findings can be listed here. • The majority of research is conducted by universities all the tasks, creating an information gap from the beginning and research centers in the USA, Japan, and China, of 2017 until now. respectively. The contribution sum of works provided For the reason outlined above, producing an up-to-date SRL by these three countries exceeds the sum of all work is a difficult task, demanding a continuous process of revision. authored by scientists from the remaining countries; However, a gap is unavoidable because of the execution time • Despite the number of solutions proposing wearables needed to conduct the proposed protocol. for environment and individual sensing, healthcare so- D. Future work lutions remain the wearable device type’s most targeted applicability area. Thus, corroborating this result, the The articles analyzed here provided a set of primary con- most relevant problems present a list of topics primarily clusions. However, a deeper screening can be made to find composed of items related to healthcare and medicine non-obvious and hidden correlations inside the dataset. For contexts; instance, the information regarding different countries can be cross-referenced with the types of developed wearables to • To solve the problems listed by each applicability area, different technologies have been taken into account by verify the main interests of each country/research group. the research. This review showed that, since 2010, ar- Moreover, considering the already extracted information, tificial intelligence has become an important tool with a clustering algorithm can be applied to identify the most high impact on wearables research. Nevertheless, other relevant and actuating research groups, highlighting their main relevant technologies were also noted (Haptic, AR/VR, interests and outputs. Root Mean Square (RMS), Internet of Things (IoT), ...) Finally, a continuous update can be considered to cover reflecting, in some ways, trends raised in other areas; recent works, despite the time required to fulfill this task. • As a natural consequence of individual sensing, the REFERENCES most common place to attach a wearable is to users’ [1] G. H. Cycle. (2018) Top trends in the gartner hype cycle for emerging limbs. Although is fairly obvious when analyzing pre- technologies, 2017. https://www.gartner.com/smarterwithgartner/ viously presented data, this work has also enumerated top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017/. Accessed: 14 may 2018. the preferred types of wearables and their positions when [2] Statista. 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