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Review

A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis

School of Innovation, Design and Engineering, Mälardalen University, 722 20 Västerås, Sweden
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(5), 1502; https://doi.org/10.3390/s20051502
Received: 4 February 2020 / Revised: 26 February 2020 / Accepted: 5 March 2020 / Published: 9 March 2020
(This article belongs to the Special Issue Wireless Body Sensors)

Abstract

The use of wearable body sensors for health monitoring is a quickly growing field with the potential of offering a reliable means for clinical and remote health management. This includes both real-time monitoring and health trend monitoring with the aim to detect/predict health deterioration and also to act as a prevention tool. The aim of this systematic review was to provide a qualitative synthesis of studies using wearable body sensors for health monitoring. The synthesis and analysis have pointed out a number of shortcomings in prior research. Major shortcomings are demonstrated by the majority of the studies adopting an observational research design, too small sample sizes, poorly presented, and/or non-representative participant demographics (i.e., age, gender, patient/healthy). These aspects need to be considered in future research work.
Keywords: health monitoring; IoT; physical activity monitoring; qualitative synthesis; remote health management; research shortcomings; sensor systems; user demography; wearable body sensors health monitoring; IoT; physical activity monitoring; qualitative synthesis; remote health management; research shortcomings; sensor systems; user demography; wearable body sensors

1. Introduction

The use of wearable body sensors for health monitoring as a means for supporting clinical and remote health monitoring in real-time and to provide health trend monitoring with the aim to predict/prevent health deterioration has the potential to lower the burden on the healthcare system and thereby reduce healthcare costs. The number of available wearable and wireless body sensors and systems are rapidly growing. Simultaneously, research on more energy-efficient and more accessible/smaller sensors for acquiring data as well as research on automatic data analysis of the Big Data, which the sensor-based systems are expected to generate, is being conducted. This advanced data analysis has the potential of generating personalized diagnoses and providing recommendations on treatments at a personalized level. While a promising area, we argue that the data collected for generating advanced data analysis algorithms need to come from participants representing the expected users of these systems.
This systematic review provides a qualitative synthesis of the articles retrieved on using wearable body sensors for health monitoring. We analyze the articles from many perspectives including author affiliations in countries, publication years, context of use, sensor category, research methodology, sample sizes, and participant demographics (i.e., age, gender, patient/healthy). This analysis has identified a number of shortcomings in prior research with respect to both sample size, but also to participant demographics where the latter strongly affects the validity of the results. These shortcomings need to be considered in future research, not only for understanding the user experience, but also to ensure that the advanced data analysis algorithms can reason on data which are representative and valid for the expected users of the systems.

2. Methodology

Following the requirements of MDPI Sensors, a systematic review following the PRISMA guidelines [1] was conducted. A total of seven databases were searched, including: Web of Science Core Collection, MEDLINE, Scopus, ScienceDirect, Academic Search Elite, ACM Digital Library, and IEEE Xplore.
The searches were conducted on 24–25 April 2019. The search phrases resulting in the identification, and addition to an EndNote database, of related articles are shown in Table 1. During the search, the keywords were changed in order to broaden or narrow the number of articles found using the previous search phrase. For example, “Ecare” or “mHealth” or “ehealth” was replaced with “care” or “Health” in the second search in Web of Science Core Collection. The same search phrase was used for MEDLINE but it resulted in thousands of hits in SCOPUS. Several additional searches aiming at limiting the number of hits were conducted resulting in "care" or "Health" being replaced with the original search phrase "ecare or mhealth or ehealth" and the exclusion of "feedback" and "pilot application". The search phrase used for Scopus resulted in no hits in Science Direct. Therefore, two less narrow searches were conducted. Variations of these phrases were used in Academic Search Elite, ACM Digital Library and IEEE Xplore.

Article Selection, Inclusion and Exclusion Criteria

The search resulted in 495 articles. Thereafter, the articles were screened in several steps using EndNote:. Thirty duplicated articles were eliminated and 288 articles were excluded after reviewing each title and abstract individually. Abstracts and articles retrieved that did not match the main research question were excluded from further consideration. For example, we excluded articles on studies using solely environmental exposure sensors or smart home sensors.
Then, pdf copies of all remaining articles were downloaded. Copies of abstracts, introductions and conclusions were extracted to OneNote after which an additional screening was conducted. The eligibility criteria for inclusion in the review were:
  • Articles should be published as a journal article or in conference proceedings.
  • Articles should consider wearable technology and monitoring.
  • Articles should present results from studies where sensor data were collected using humans. Alternatively, the articles present information on a system where the user trial is planned for but not conducted yet.
  • Articles should be in English.
Overviewing the remaining 177 articles, it was found that the number of publications relating to some health conditions, henceforth called article categories, was low. Therefore, no articles were excluded based on publication year. In addition, we excluded the numerous review articles from further analysis as they cannot be considered original research, i.e., the review articles retrieved were excluded since they do not directly report on a conducted study of people or on the planning of such a study. Publications that met the inclusion criteria, and therefore, considered for further reviewing were 73. The study selection process is depicted in Figure 1.

3. Qualitative Synthesis

Inspired by Kekade et al.’s review from 2018 [2], we conducted a qualitative synthesis of the 73 included research articles. They were published between 2010 and 2019, i.e., spanning approx. 9.5 years, among which one article was published in 2010, two in 2011, seven in 2012, two in 2013, seven in 2014, twelve in 2015, nine in 2016, fourteen in 2017, fourteen in 2018 and five before April 24th 2019, see Figure 2. In average, 7.6 articles were published per year during the period 2010–2018. The authors of the 73 research articles were affiliated in 32 countries representing six continents (Africa, Asia, Australia, Europe, North America and South America). See Figure 3 and Figure 4 for further information on which countries authors are affiliated in and the number of publications per country with affiliated authors. The articles were sorted into the following article categories: Asthma/COPD, Cardiovascular diseases, Diabetes and nutrition, Gait and fall, Neurological diseases, Physical activity recognition, Rehabilitation, and Stress and sleep. All articles not directly related to any of the aforementioned article categories were sorted into an article category named Additional. Figure 5 depicts the category-wise distribution of the selected articles. Studying the distribution of articles related to health and physical activity monitoring respectively, it can be seen that 47 % of the articles were related to health (Asthma/COPD, Cardiovascular diseases, Diabetes and Nutrition, Neurological diseases, and Stress and sleep). As much as 39 % of the articles were related to physical activity monitoring (Gait and fall, Physical activity recognition, and Rehabilitation). It is unclear why such a large portion of the articles were related to physical activity monitoring. Possible reasons include that it is easier to monitor physical activity using sensors whereas measures relating to health, e.g., vital signs, need to be provided in a more timely manner.
Sixty research articles reported on studies conducted with people at some level, these are reported in Table 2. We categorized the sensors according to the sensor categories used in [2], namely, physical activity, vital signs, electrocardiography (ECG) and other. Studies reporting on devices measuring movement or activity were classified under the sensor category physical activity. Vital signs include the parameters: blood pressure (BP), body temperature (BT), respiratory rate (RR), heart rate (HR)/pulse, and peripheral oxygen saturation (SpO2). Studies measuring ECG were classified under ECG. Finally, studies using sensors for diabetes, swallowing, etc., or a combination of sensors from several sensor categories were classified under the sensor category other. The sensor categories physical activity and other include 23 studies each, vital signs includes three studies, and ECG includes ten studies reported upon in seven research articles.
Similarly to Kekade et al. 2018 [2], we also assessed the studies’ reporting of research design (Table 2), and the reported participant demography, i.e., number of participants, age, gender and the distribution of healthy participants and patients (see Section 3.1, Section 3.2, Section 3.3 and Section 3.4 and Table 3). Many studies presented the participant demographics poorly, or not at all [3,4,5,6,7,8,9,10,11,12]. Rather than excluding these from the tables, we indicate missing information with a “-”. However, we question the fact that all these studies were accepted for publication without providing any information on the participants. Our findings are further discussed in Section 4.
For completeness, the remaining 13 articles not listed in Table 2 and Table 3 were distributed over eight article categories: Asthma/COPD [63], Cardiovascular diseases [64], Gait and fall [65,66,67], Neurological diseases [68], Physical activity recognition [69], Rehabilitation [70], Stress and sleep [71], and Additional [72,73,74,75]. Six articles report on systems where studies are upcoming [63,64,72,73,74,75]. One of them [64] is a continuation of the study reported in [23]. Three articles report on studies using datasets [66,67,69]. Two articles report on qualitative studies of observational and/or interview nature [68,70]. The continuation of the qualitative study [70] is reported upon in [48]. The evaluation in [65] is not clearly presented and the system developed in [71] uses wearable body sensors only to collect ground truth data for a contactless sleep monitoring system. Therefore, [71] was excluded from further qualitative analysis.

3.1. Research Methodology

Table 2 reports on the four research designs identified while analyzing the research articles: case-control, crossover, randomized control and observational. Articles categorized as adopting a case-control research design are prospective and include studies with two groups. In most articles, one group is a healthy control group and the other a group sharing an illness. However, in this review, also articles comparing the measures for two distinct groups (e.g., non-shift workers in rural and urban areas) have been categorized as adopting a case-control research design. Articles categorized as adopting a randomized-control research design have participants with the same background being randomly assigned to one of two study conditions. One article has been categorized as a crossover study [59], the participants have experienced both study conditions but in randomized order. The articles categorized as being observational are typically conducted in a controlled fashion during which data are collected. In this review, the majority of the articles were categorized as being observational. A few articles adopted a case-control [19,41,42,50,54] or randomized control research design [16,28,56]. For some articles [11,18,21,23,24,58], information provided on how the experiments were conducted was not sufficient for determining the research design adopted.
Studying the number of participants included in the studies, we first summarized the number of participants in the cases where an article reported on several smaller studies. It can be seen from Figure 6 that 57% of the studies were conducted with up to 20 participants and that 30% were conduced with 10 or fewer participants. Only 40% of the studies were conducted with 21 or more participants (22% collected 21-50 participants, 13% had 51–100 participants leaving 5% with more than 100 participants).
Looking more closely into each article category, Figure 7 shows that the majority of the studies within the categories Asthma/COPD, Gait and fall, Physical activity recognition, Rehabilitation, Stress and sleep, and Additional were conducted with up to 20 participants. The studies with more than 100 participants fall within the categories Asthma/COPD, Cardiovascular diseases, and Diabetes and nutrition. Studies with 51–100 participants were conducted within the categories Cardiovascular diseases, Gait and fall, Neurological diseases, Rehabilitation, Stress and sleep, and Additional.
To make technical validations that a sensor is working, a small number of participants can be accepted. However, to be used in clinical investigations, power calculations taking the research question into account should be used to decide the number of needed participants.

3.2. Age Distribution

Information on the participants’ age was provided in 35/60 (58.3%) of the articles reporting on data collection studies with people (Table 3). Another two articles [21,23] provided the information on age for only one of the study groups. A very limited number of studies were conducted with people where μ a g e > 65 [40,53] or μ a g e > 60 [13,14,48,51]. Two studies [30,39] were conducted with one young group and one group where μ a g e > 65, whereas μ a g e > 60 for one of the groups in [17]. Two articles report on studies with large age ranges where some participants exceed 65 years of age (16–72 and 20–73 in [22], and 40–70 in [35]).
Studying the articles from an article category perspective, none of the studies reporting on the categories Cardiovascular diseases, Diabetes and nutrition, Other or Stress and sleep was conducted with participants where μ a g e > 60. The categories Asthma/COPD, Gait and fall, Neurological diseases, and Rehabilitation include some studies with this age group. None of the studies within the Physical activity recognition category report on the participants’ age.

3.3. Gender Distribution

Information on the participants’ gender was provided in 33/60 (55%) of the articles reporting on data collection studies with people (Table 3). Three more articles [9,13,23] reported on studies with more than one group but not the gender for all groups.
Studying the articles from an article category perspective, all Asthma/COPD studies except [13] provided full information on gender distribution. The latter, [13] also reports on a study with a subset of the participants without providing information on gender. Regarding cardiovascular diseases studies, only [20,23] provided full information on gender distribution. Another 20 want to participate in screening although the study described in [23] is not approved yet by an ethical committee. All but one study within Diabetes and nutrition report on gender. The majority of the studies within Gait and fall contain information on gender. More than half (57%) of the articles on Neurological diseases and 50% of the articles on Other present information on gender. Regarding the category Physical activity recognition, only one article [47] provides full information on gender. Another article, [9] provides information on gender for one of their four sub-studies. The majority (80%) of the Rehabilitation studies and 50% of the Stress and Sleep studies provide gender information.
Studying the articles from a gender distribution perspective, the vast majority of the participants in the studies reporting on Asthma/COPD are men. For Cardiovascular diseases, [20] had a rather even gender distribution, [23] reported on gender in a study aiming at validating a measurement protocol and for evaluating the usability and acceptance level of an ICT equipment. The majority of the participants were men. A similar pattern is observed for Diabetes and nutrition, Gait and fall, Neurological diseases, Other, Rehabilitation and Stress and sleep. Women are only in majority for one of the groups in the Gait and fall study [30], and the Rehabilitation studies [41,42,48].

3.4. Tests on Patients and Healthy Users

Information on whether the participants were patients and/or healthy was provided in 39/60 (65%) of the articles (Table 3). An additional four studies, [9,21,22,38] present the distribution of patients and/or healthy for some of the reported sub-studies. Two groups including 84 participants in total were representing patients and healthy participants in [19]. Seven articles [13,17,19,23,41,42,50] report on the conduction of studies with both patients and healthy. Two articles [9,22] contain results from several sub-studies and while not providing patient/healthy information for all sub-studies, claim to have used both patients and healthy participants during data collection. For several article categories, many of the studies reported information on both patients and healthy users.
Studying the articles from a health perspective, i.e., looking particularly at the article categories Asthma/COPD, Cardiovascular diseases, Diabetes and nutrition, Neurological diseases, and Stress and Sleep, the reporting and/or use of patients/healthy participants varies. Almost all participants in studies on Asthma/COPD and Neurological diseases were patients. Surprisingly, the Cardiovascular diseases [20,23] were conducted solely with healthy participants while another [21] and three of the sub-studies in [21,22] lack information on whether the participants were healthy or patients. Regarding Diabetes and Nutrition, two works [24,28] were conducted with patients, one study [27] was conducted with healthy participants while two articles [25,26] lack this information. Finally, regarding Stress and sleep, none of the studies report on studies with patients. Three articles [55,57,58] were conducted with healthy participants while the remaining three articles lack this information.
Studying the articles from a physical activity perspective, i.e., looking particularly at the article categories Gait and fall, Physical activity monitoring and Rehabilitation. No information on whether the participants were healthy or patients were provided in the articles falling under the Physical activity monitoring article category. None of the studies within Gait and fall used patients. The picture is mixed for the category Rehabilitation, two studies were conducted solely with patients [48,51] whereas [50] reports on two sub-studies conducted with patients and healthy participants respectively. One work [52] was conducted solely with healthy participants and two works [49,53] do not provide this information.

4. Discussion and Conclusions

In this systematic review, we provide a qualitative synthesis on retrieved articles on using wearable body sensors for health monitoring. The articles found were categorized as relating to: Asthma/COPD, Cardiovascular diseases, Diabetes and Nutrition, Gait and fall, Neurological diseases, Physical activity recognition, Rehabilitation, Stress and sleep, and Additional. Section 3 provided a qualitative synthesis of the studies with respect to research methodology and participant demography, i.e., number of participants, age, gender and the distribution of healthy participants and patients. Using this information, we have identified a number of shortcomings. Below follows a discussion on these shortcomings in relation to prior research.
There are many age-related health issues such as changing biological factors, the onset of illnesses which are often chronic and the decline of cognitive abilities. For example, “fall prediction is a challenging problem due to the combination of intrinsic and extrinsic fall risk factors that contribute to a fall. Intrinsic factors include age, fall history, mobility impairments, sleep disturbances, and neurological disorders", pp. 1 [76]. It is reported in [77] that 35% of non-institutionalized adults had abnormal gait and that sleep disturbances are very common among older people. Further, chronic conditions affect physical activity levels, and activities such as rising from a chair is demanding for older people [77]. It is clear that the whole motion pattern changes with age and the onset of illnesses related to the human locomotor system. Yet, the majority of the studies focusing on gait and fall in this review were simulations that include none or few old participants. This shortcoming is also discussed in [76], “It is evident that existing systems have mainly been tested in laboratory environments with controlled conditions and do not include frequent fallers and aging adults as test subjects.[..] future work should focus on longitudinal studies of fall detection and prediction systems in real-life conditions on a diverse group that includes frequent fallers, aging adults, and persons with neurological disorders.” p.8 [76]. Not studying the sensor systems in real-life conditions affect the validity of the results since the performance is not studied in realistic conditions. The low number of studies with older people is also a shortcoming since age-related issues are not taken into consideration to a sufficient degree.
There are many differences between the two genders. As a first example, we want to mention the American Heart Association’s (AHA) scientific statement from 2016 [78] on acute myocardial infarction (AMI) in women. “Sex differences occur in the pathophysiology and clinical presentation of MI and affect treatment delays.”, p. 932 [78]. Further, AHA reports that the same perfusion therapies are recommended despite the fact that the risk of bleeding or other complications is higher among women. Further, women are being under-treated with guideline recommendations. This results in increased readmission, re-infarction, and death rates during the first year after a myocardial infarction. Cardiac rehabilitation is also underused and under-prescribed among women [78]. On the same lines, the results of a cohort study [79] with almost 5000 patients μ a g e > 65 who were admitted to 366 US hospitals in the period 2003–2009, has found that women are less likely to receive optimal care at discharge. Yet, only two of the studies retrieved within the category Cardiovascular diseases provide information on the participants’ gender. This is not the only shortcoming for studies on Cardiovascular diseases however. Several studies, or sub-studies, were conducted with very large age spans without the provision of a mean age. Others were conducted with young people or lacks information on age. Further, several works report on studies with healthy participants.
Hence, studies taking both genders into consideration, but also the age factor, are highly desired in the category Cardiovascular diseases. Not including information on gender and/or not considering gender/sex during data collection is a shortcoming regardless of the category to which a study belongs. It is argued in [80] that there are areas were specific data on women is lacking while specific data on men is missing in other areas.
Regitz-Zagrosek [80] outlines a number of differences between men and women. These include: women more frequently having anemia, women suffering from coronary artery disease in average ten years later than men, a higher frequency of boys having asthma in young ages while the frequency changes to young adulthood, diabetes increasing the risk for coronary heart disease more among women, and osteoporosis being more frequent in women but under-diagnosed in men. Osteoporosis disease is characterized by a decreased bone mass density and a disrupted normal trabecular architecture reducing bone strength [81]. Therefore, Osteoporosis increases the risk of fractures after a fall but no symptoms of the disease are shown until a fracture occurs [80]. According to [81], there are several factors relating to Osteoporosis which increases the risk of falling. These include the fear of falling, which increases the risk of falling [82,83]. In addition, [81] reports on studies discussing women with osteoporosis or low bone mass where fear of falling is associated with more falls [84], and the confidence in balance is related to balance and mobility [85]. Further, [84] reports that an increased thoracic kyphosis is associated with recent falls among women with Osteopororosis. I.e., women with thoracic kyphosis were more likely to have had a recent fall. Thoracic kyphosis is an abnormal convex curvature of the spine at chest height which is much more common among older women than men due to estrogen losses [86]. All these works [81,82,83,84,85] date from 2004-2011, hence it is astonishing that some articles retrieved within the article category Gait and fall have not reported information on gender and that some other articles were conducted solely with men. Hence, we argue that future studies in the categories discussed in this article must take gender into consideration. This shortcoming was also highlighted in [2].
Undoubtedly, healthy participants and patients differ in many aspects. Yet, only 65% of the studies overall reported this information. A positive example here is the fact that the studies reported upon in the category Asthma/COPD were conducted almost entirely with patients. This indicates that the results in this area are reliable. On the contrary, none of the studies within Gait and fall, or Stress and sleep have reported that the studies were conducted with patients. Also [76,77] have previously discussed the shortcoming of not conducting studies with patients in the category Gait and fall. Considering the research question for this review article, we question the fact that 35% of the retrieved articles lack information on whether the participants were healthy or patients. We argue that the use of healthy participants, or not providing this information, affect the validity of the study results. Future studies need to consider the inclusion of patients to a further extent.
Studying the sample size in the reported studies, 56% of the articles report on studies conducted with up to 20 participants, and only 20% of the articles report on studies conducted with 51 or more participants. The distribution of numbers vary between categories. The majority of the studies reported in the categories Asthma/COPD, Gait and fall, Physical activity recognition, Rehabilitation, and Stress and sleep were conducted with up to 20 participants. We find the overall low number of participants a shortcoming and recommend that future studies are conducted with larger study samples. However, taking demographic factors, i.e., age, gender and healthy/patient into consideration is highly needed prior to increasing the sample sizes in studies on health monitoring using wearable body sensors.

Author Contributions

Search phrase, inclusion and exclusion criteria, A.K. and M.L.; literature search, A.K.; initial screening of articles, A.K.; selection of articles, A.K. and M.L.; article categorization, A.K.; tables and figures, A.K.; analysis of material in tables and figures, A.K. and M.L.; original draft preparation, A.K.; review and editing, A.K. and M.L. All authors have read and agree to the published version of the manuscript.

Funding

This research was conducted within the scope of the ESS-H+ (Embedded Sensor Systems for Health Plus). The project is funded by the Swedish Knowledge Foundation (project number: 20180158).

Conflicts of Interest

The authors declare no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; the PRISMA Group. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Ann. Internal Med. 2009, 151, 264–269. [Google Scholar] [CrossRef]
  2. Kekade, S.; Hseieh, C.H.; Islam, M.M.; Atique, S.; Mohammed Khalfan, A.; Li, Y.C.; Abdul, S.S. The usefulness and actual use of wearable devices among the elderly population. Comput. Methods Programs Biomed. 2018, 153, 137–159. [Google Scholar] [CrossRef]
  3. Li, Y.; Li, S.; Song, H.; Shao, B.; Yang, X.; Deng, N. Noninvasive blood pressure estimation with peak delay of different pulse waves. Int. J. Distrib. Sens. Netw. 2019, 15. [Google Scholar] [CrossRef]
  4. Wu, X.; Wang, Y.; Chien, C.; Pottie, G. Self-calibration of sensor misplacement based on motion signatures. In Proceedings of the 2013 IEEE International Conference on Body Sensor Networks, Cambridge, MA, USA, 6–9 May 2013; pp. 1–5. [Google Scholar] [CrossRef]
  5. Castro, D.; Coral, W.; Rodriguez, C.; Cabra, J.; Colorado, J. Wearable-Based Human Activity Recognition Using an IoT Approach. J. Sens. Actuator Netw. 2017, 6, 28. [Google Scholar] [CrossRef]
  6. Rodriguez, C.; Castro, D.M.; Coral, W.; Cabra, J.L.; Velasquez, N.; Colorado, J.; Mendez, D.; Trujillo, L.C.; ACM. IoT system for Human Activity Recognition using BioHarness 3 and Smartphone. In Proceedings of the International Conference on Future Networks and Distributed Systems, Cambridge, UK, 19–20 July 2017. [Google Scholar] [CrossRef]
  7. Doron, M.; Bastian, T.; Maire, A.; Dugas, J.; Perrin, E.; Gris, F.; Guillemaud, R.; Deschamps, T.; Bianchi, P.; Caritu, Y.; et al. Estimation of physical activity monitored during the day-to-day life by an autonomous wearable device (SVELTE project). In Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 3–7 July 2013; pp. 4629–4632. [Google Scholar] [CrossRef]
  8. Xu, J.Y.; Chang, H.I.; Chien, C.; Kaiser, W.J.; Pottie, G.J. Context-driven, prescription-based personal activity classification: Methodology, architecture, and end-to-end implementation. IEEE J. Biomed. Health Inform. 2014, 18, 1015–1025. [Google Scholar] [CrossRef]
  9. Xu, J.Y.; Wang, Y.; Barrett, M.; Dobkin, B.; Pottie, G.J.; Kaiser, W.J. Personalized multilayer maily life profiling through context enabled activity classification and motion reconstruction: An integrated system approach. IEEE J. Biomed. Health Inform. 2016, 20, 177–188. [Google Scholar] [CrossRef] [PubMed]
  10. Velicu, O.R.; Madrid, N.M.; Seepold, R.; IEEE. Experimental sleep phases monitoring. In Proceedings of the 2016 3rd IEEE Embs International Conference on Biomedical and Health Informatics, Las Vegas, NV, USA, 24–27 February 2016; pp. 625–628. [Google Scholar]
  11. Seeger, C.; Buchmann, A.; Van Laerhoven, K. An Event-based BSN Middleware That Supports Seamless Switching Between Sensor Configurations. In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, Miami, Fl, USA, 28–30 January 2012; pp. 503–512. [Google Scholar] [CrossRef]
  12. Wannenburg, J.; Malekian, R. Body Sensor Network for Mobile Health Monitoring, a Diagnosis and Anticipating System. IEEE Sens. J. 2015, 15, 6839–6852. [Google Scholar] [CrossRef]
  13. Bonnevie, T.; Gravier, F.E.; Elkins, M.; Dupuis, J.; Prieur, G.; Combret, Y.; Viacroze, C.; Debeaumont, D.; Robleda-Quesada, A.; Quieffin, J.; et al. People undertaking pulmonary rehabilitation are willing and able to provide accurate data via a remote pulse oximetry system: a multicentre observational study. J. Physiother. 2019, 65, 28–36. [Google Scholar] [CrossRef] [PubMed]
  14. Caulfield, B.; Kaljo, I.; Donnelly, S. Use of a consumer market activity monitoring and feedback device improves exercise capacity and activity levels in COPD. In Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 26–30 August 2014; pp. 1765–1768. [Google Scholar] [CrossRef]
  15. Estrada, L.; Torres, A.; Sarlabous, L.; Jané, R. Evaluating respiratory muscle activity using a wireless sensor platform. In Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 16–20 August 2016; pp. 5769–5772. [Google Scholar] [CrossRef]
  16. Katsaras, T.; Milsis, A.; Rizikari, M.; Saoulis, N.; Varoutaki, E.; Vontetsianos, A. The use of the “Healthwear” wearable system in chronic patients’ early hospital discharge: Control randomized clinical trial. In Proceedings of the 2011 5th International Symposium on Medical Information and Communication Technology, Montreux, Switzerland, 27–30 March 2011; pp. 143–146. [Google Scholar] [CrossRef]
  17. Naranjo-Hernández, D.; Talaminos-Barroso, A.; Reina-Tosina, J.; Roa, L.M.; Barbarov-Rostan, G.; Cejudo-Ramos, P.; Márquez-Martín, E.; Ortega-Ruiz, F. Smart vest for respiratory rate monitoring of copd patients based on non-contact capacitive sensing. Sensors 2018, 18, 2144. [Google Scholar] [CrossRef]
  18. Huang, A.; Xu, W.; Li, Z.; Xie, L.; Sarrafzadeh, M.; Li, X.; Cong, J. System Light-Loading Technology for mHealth: Manifold-Learning-Based Medical Data Cleansing and Clinical Trials in WE-CARE Project. IEEE J. Biomed. Health Inform. 2014, 18, 1581–1589. [Google Scholar] [CrossRef] [PubMed]
  19. Huang, A.; Chen, C.; Bian, K.; Duan, X.; Chen, M.; Gao, H.; Meng, C.; Zheng, Q.; Zhang, Y.; Jiao, B.; et al. WE-CARE: An intelligent mobile telecardiology system to enable mHealth applications. IEEE J. Biomed. Health Inform. 2014, 18, 693–702. [Google Scholar] [CrossRef] [PubMed]
  20. Javaid, A.Q.; Chang, I.S.; Mihailidis, A. Ballistocardiogram Based Identity Recognition: Towards Zero-Effort Health Monitoring in an Internet-of-Things (IoT) Environment. In Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 18–21 July 2018; Volume 2018, pp. 3326–3329. [Google Scholar] [CrossRef]
  21. Raad, M.W.; Sheltami, T.; Deriche, M. A Ubiquitous Telehealth System for the Elderly. In Internet of Things: User-Centric Iot, Pt I; Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering; Giaffreda, R., Vieriu, R.L., Pasher, E., Bendersky, G., Jara, A.J., Rodrigues, J., Dekel, E., Mandler, B., Eds.; Springer: Berlin, Germany, 2015; Volume 150, pp. 159–166. [Google Scholar] [CrossRef]
  22. Simjanoska, M.; Gjoreski, M.; Gams, M.; Bogdanova, A.M. Non-invasive blood pressure estimation from ECG using machine learning techniques. Sensors 2018, 18, 1160. [Google Scholar] [CrossRef] [PubMed]
  23. Susič, T.P.; Stanič, U. Penetration of the ICT technology to the health care primary sector—Ljubljana PILOT. In Proceedings of the 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, 30 May–3 June 2016; pp. 436–441. [Google Scholar] [CrossRef]
  24. Al-Taee, M.A.; Al-Nuaimy, W.; Al-Ataby, A.; Muhsin, Z.J.; Abood, S.N.; IEEE. Mobile Health Platform for Diabetes Management Based on the Internet-of-Things. In Proceedings of the 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, New York, NY, USA, 3–5 November 2015. [Google Scholar] [CrossRef]
  25. Alshurafa, N.; Kalantarian, H.; Pourhomayoun, M.; Sarin, S.; Liu, J.J.; Sarrafzadeh, M. Non-invasive monitoring of eating behavior using spectrogram analysis in a wearable necklace. In Proceedings of the 2014 IEEE Healthcare Innovation Conference (HIC), Seattle, WA, USA, 8–10 October 2014; pp. 71–74. [Google Scholar] [CrossRef]
  26. Alshurafa, N.; Kalantarian, H.; Pourhomayoun, M.; Liu, J.J.; Sarin, S.; Shahbazi, B.; Sarrafzadeh, M. Recognition of Nutrition Intake Using Time-Frequency Decomposition in a Wearable Necklace Using a Piezoelectric Sensor. IEEE Sens. J. 2015, 15, 3909–3916. [Google Scholar] [CrossRef]
  27. Dong, B.; Biswas, S. Meal-time and duration monitoring using wearable sensors. Biomed. Signal Process. Control 2017, 32, 97–109. [Google Scholar] [CrossRef]
  28. Onoue, T.; Goto, M.; Kobayashi, T.; Tominaga, T.; Ando, M.; Honda, H.; Yoshida, Y.; Tosaki, T.; Yokoi, H.; Kato, S.; et al. Randomized controlled trial for assessment of Internet of Things system to guide intensive glucose control in diabetes outpatients: Nagoya Health Navigator Study protocol. Nagoya J. Med. Sci. 2017, 79, 323–329. [Google Scholar] [CrossRef] [PubMed]
  29. Atallah, L.; Wiik, A.; Jones, G.G.; Lo, B.; Cobb, J.P.; Amis, A.; Yang, G.Z. Validation of an ear-worn sensor for gait monitoring using a force-plate instrumented treadmill. Gait Posture 2012, 35, 674–676. [Google Scholar] [CrossRef]
  30. Godfrey, A.; Din, S.D.; Barry, G.; Mathers, J.C.; Rochester, L. Within trial validation and reliability of a single tri-axial accelerometer for gait assessment. In Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 26–30 August 2014; pp. 5892–5895. [Google Scholar] [CrossRef]
  31. Lee, J.K.; Robinovitch, S.N.; Park, E.J. Inertial Sensing-Based Pre-Impact Detection of Falls Involving Near-Fall Scenarios. IEEE Trans. Neural Syst. Rehabil. Eng. 2015, 23, 258–266. [Google Scholar] [CrossRef]
  32. Liang, D.; Zhao, G.; Guo, Y.; Wang, L. Pre-impact & impact detection of falls using wireless Body Sensor Network. In Proceedings of the 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, Hong Kong, China, 5–7 January 2012; pp. 763–766. [Google Scholar] [CrossRef]
  33. Liang, S.; Chu, T.; Lin, D.; Ning, Y.; Li, H.; Zhao, G. Pre-impact Alarm System for Fall Detection Using MEMS Sensors and HMM-based SVM Classifier. In Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 18–21 July 2018; pp. 4401–4405. [Google Scholar] [CrossRef]
  34. Paiman, C.; Lemus, D.; Short, D.; Vallery, H. Observing the State of Balance with a Single Upper-Body Sensor. Front. Robot. AI 2016, 3. [Google Scholar] [CrossRef]
  35. Tino, A.; Carvalho, M.; Preto, N.F.; McConville, K.M.V. Wireless vibrotactile feedback system for postural response improvement. In Proceedings of the 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Boston, MA, USA, 30 August–3 September 2011; Volume 2011, pp. 5203–5206. [Google Scholar] [CrossRef]
  36. Williams, B.; Allen, B.; True, H.; Fell, N.; Levine, D.; Sartipi, M.; IEEE. A Real-time, Mobile Timed Up and Go System. In Proceedings of the 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Cambridge, MA, USA, 9–12 June 2015. [Google Scholar] [CrossRef]
  37. Wu, Y.; Su, Y.; Feng, R.; Yu, N.; Zang, X. Wearable-sensor-based pre-impact fall detection system with a hierarchical classifier. Measurement 2019, 140, 283–292. [Google Scholar] [CrossRef]
  38. Zhao, G.; Mei, Z.; Liang, D.; Kamen, I.; Guo, Y.; Wang, Y.; Wang, L. Exploration and Implementation of a Pre-Impact Fall Recognition Method Based on an Inertial Body Sensor Network. Sensors 2012, 12, 15338–15355. [Google Scholar] [CrossRef]
  39. Zhong, R.; Rau, P.L.P.; Yan, X. Gait Assessment of Younger and Older Adults with Portable Motion-Sensing Methods: A User Study. Mob. Inf. Syst. 2019, 2019. [Google Scholar] [CrossRef]
  40. Giuberti, M.; Ferrari, G.; Contin, L.; Cimolin, V.; Azzaro, C.; Albani, G.; Mauro, A. Automatic UPDRS Evaluation in the Sit-to-Stand Task of Parkinsonians: Kinematic Analysis and Comparative Outlook on the Leg Agility Task. IEEE J. Biomed. Health Inform. 2015, 19, 803–814. [Google Scholar] [CrossRef] [PubMed]
  41. Gong, J.; Lach, J.; Qi, Y.; Goldman, M.D. Causal analysis of inertial body sensors for enhancing gait assessment separability towards multiple sclerosis diagnosis. In Proceedings of the 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Cambridge, MA, USA, 9–12 June 2015. [Google Scholar] [CrossRef]
  42. Gong, J.; Qi, Y.; Goldman, M.D.; Lach, J. Causality Analysis of Inertial Body Sensors for Multiple Sclerosis Diagnostic Enhancement. IEEE J. Biomed. Health Inform. 2016, 20, 1273–1280. [Google Scholar] [CrossRef] [PubMed]
  43. Kuusik, A.; Alam, M.M.; Kask, T.; Gross-Paju, K. Wearable m-assessment system for neurological disease patients. In Proceedings of the 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), Singapore, Singapore, 5–8 February 2018; pp. 201–206. [Google Scholar] [CrossRef]
  44. Sok, P.; Xiao, T.; Azeze, Y.; Jayaraman, A.; Albert, M.V. Activity recognition for incomplete spinal cord injury subjects using hidden markov models. IEEE Sens. J. 2018, 18, 6369–6374. [Google Scholar] [CrossRef]
  45. Stamate, C.; Magoulas, G.D.; Kueppers, S.; Nomikou, E.; Daskalopoulos, I.; Luchini, M.U.; Moussouri, T.; Roussos, G. Deep learning Parkinson’s from smartphone data. In Proceedings of the 2017 IEEE International Conference on Pervasive Computing and Communications, PerCom 2017, Kona, HI, USA, 13–17 March 2017; pp. 31–40. [Google Scholar] [CrossRef]
  46. Stamate, C.; Magoulas, G.D.; Kueppers, S.; Nomikou, E.; Daskalopoulos, I.; Jha, A.; Pons, J.S.; Rothwell, J.; Luchini, M.U.; Moussouri, T.; et al. The cloudUPDRS app: A medical device for the clinical assessment of Parkinson’s Disease. Pervasive Mob. Comput. 2018, 43, 146–166. [Google Scholar] [CrossRef]
  47. Rednic, R.; Gaura, E.; Brusey, J.; Kemp, J. Wearable posture recognition systems: Factors affecting performance. In Proceedings of the 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, Hong Kong, China, 5–7 January 2012; pp. 200–203. [Google Scholar] [CrossRef]
  48. Argent, R.; Slevin, P.; Bevilacqua, A.; Neligan, M.; Daly, A.; Caulfield, B. Wearable sensor-based exercise biofeedback for orthopaedic rehabilitation: A mixed methods user evaluation of a prototype system. Sensors 2019, 19, 432. [Google Scholar] [CrossRef]
  49. Banos, O.; Moral-Munoz, J.A.; Diaz-Reyes, I.; Arroyo-Morales, M.; Damas, M.; Herrera-Viedma, E.; Hong, C.S.; Lee, S.; Pomares, H.; Rojas, I.; et al. MDurance: A novel mobile health system to support trunk endurance assessment. Sensors 2015, 15, 13159–13183. [Google Scholar] [CrossRef]
  50. Lee, S.I.; Adans-Dester, C.P.; Grimaldi, M.; Dowling, A.V.; Horak, P.C.; Black-Schaffer, R.M.; Bonato, P.; Gwin, J.T. Enabling stroke rehabilitation in home and community settings: A wearable sensor-based approach for upper-limb motor training. IEEE J. Transl. Eng. Health Med. 2018, 6. [Google Scholar] [CrossRef]
  51. Timmermans, A.A.A.; Seelen, H.A.M.; Geers, R.P.J.; Saini, P.K.; Winter, S.; te Vrugt, J.; Kingma, H. Sensor-Based Arm Skill Training in Chronic Stroke Patients: Results on Treatment Outcome, Patient Motivation, and System Usability. IEEE Trans. Neural Syst. Rehabil. Eng. 2010, 18, 284–292. [Google Scholar] [CrossRef]
  52. Whelan, D.F.; O’Reilly, M.A.; Ward, T.E.; Delahunt, E.; Caulfield, B. Technology in rehabilitation: Comparing personalised and global classification methodologies in evaluating the squat exercise with wearable IMUs. Methods Inf. Med. 2017, 56, 361–369. [Google Scholar] [CrossRef]
  53. Xu, J.K.; Lee, U.H.; Bao, T.; Huang, Y.J.; Sienko, K.H.; Shull, P.B.; IEEE. Wearable sensing and haptic feedback research platform for gait retraining. In Proceedings of the 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks, Eindhoven, The Netherlands, 9–12 May 2017; pp. 125–128. [Google Scholar]
  54. Lin, C.; Gamble, J.; Yang, Y.; Wang, J. Estimating the influence of chronotype and social zeitgebers on circadian rhythms using an accelerometer-based sensor network. In Proceedings of the 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, Hong Kong, China, 5–7 January 2012; pp. 285–288. [Google Scholar] [CrossRef]
  55. Nakamura, T.; Goverdovsky, V.; Morrell, M.J.; Mandic, D.P. Automatic Sleep Monitoring Using Ear-EEG. IEEE J. Transl. Eng. Health Med. 2017, 5, 1–8. [Google Scholar] [CrossRef] [PubMed]
  56. Parnandi, A.; Gutierrez-Osuna, R. Physiological Modalities for Relaxation Skill Transfer in Biofeedback Games. IEEE J. Biomed. Health Inform. 2017, 21, 361–371. [Google Scholar] [CrossRef] [PubMed]
  57. Uday, S.; Jyotsna, C.; Amudha, J.; IEEE. Detection of Stress using Wearable Sensors in IoT Platform. In Proceedings of the 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India, 20–21 April 2018; pp. 492–498. [Google Scholar] [CrossRef]
  58. Umemura, G.S.; Pinho, J.P.; Furtado, F.; Gonçalves, B.S.B.; Fomer-Cordero, A. Comparison of sleep parameters assessed by actigraphy of healthy young adults from a small town and a megalopolis in an emerging country. In Proceedings of the 2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT), Bethesda, MD, USA, 6–8 November 2017; pp. 200–203. [Google Scholar] [CrossRef]
  59. Ayzenberg, Y.; Picard, R.W. FEEL: A System for Frequent Event and Electrodermal Activity Labeling. IEEE J. Biomed. Health Inform. 2014, 18, 266–277. [Google Scholar] [CrossRef] [PubMed]
  60. Pagán, J.; Risco-Martín, J.L.; Moya, J.M.; Ayala, J.L. Grammatical Evolutionary Techniques for Prompt Migraine Prediction. In Proceedings of the Genetic and Evolutionary Computation Conference, Denver, CO, USA, 20–24 July 2016; pp. 973–980. [Google Scholar] [CrossRef]
  61. Rawashdeh, M.; Al-Qurishi, M.; Al-Rakhami, M.; Al-Quraishi, M.S. A multimedia cloud-based framework for constant monitoring on obese patients. In Proceedings of the 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Hong Kong, China, 10–14 July 2017; pp. 139–144. [Google Scholar] [CrossRef]
  62. Wu, W.; Pirbhulal, S.; Sangaiah, A.K.; Mukhopadhyay, S.C.; Li, G. Optimization of signal quality over comfortability of textile electrodes for ECG monitoring in fog computing based medical applications. Future Gener. Comput. Syst. 2018, 86, 515–526. [Google Scholar] [CrossRef]
  63. Buonocore, C.M.; Rocchio, R.A.; Roman, A.; King, C.E.; Sarrafzadeh, M. Wireless Sensor-Dependent Ecological Momentary Assessment for Pediatric Asthma mHealth Applications. In Proceedings of the Second IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, Philadelphia, PA, USA, 17–19 July 2017; pp. 137–146. [Google Scholar] [CrossRef]
  64. Depolli, M.; Avbelj, V.; Trobec, R.; Kališnik, J.M.; Tadej, K.; Susič, A.P.; Stanič, U.; Semeja, A. PCARD platform for mhealth monitoring. Informatica 2016, 40, 117–123. [Google Scholar]
  65. Ghazal, M.; Khalil, Y.A.; Dehbozorgi, F.J.; Alhalabi, M.T. An integrated caregiver-focused mHealth framework for elderly care. In Proceedings of the 11th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2015, Abu Dhabi, UAE, 19–21 October 2015; pp. 238–245. [Google Scholar] [CrossRef]
  66. Anupama, K.R.; Adarsh, R.; Pahwa, P.; Ramachandran, A. Machine Learning-Based Techniques for Fall Detection in Geriatric Healthcare Systems. In Proceedings of the 2018 9th International Conference on Information Technology in Medicine and Education (ITME), Hangzhou, China, 19–21 October 2018; pp. 232–237. [Google Scholar] [CrossRef]
  67. Boutellaa, E.; Kerdjidj, O.; Ghanem, K. Covariance matrix based fall detection from multiple wearable sensors. J. Biomed. Inform. 2019, 103189. [Google Scholar] [CrossRef]
  68. Memedi, M.; Tshering, G.; Fogelberg, M.; Jusufi, I.; Kolkowska, E.; Klein, G. An Interface for IoT: Feeding Back Health-Related Data to Parkinson’s Disease Patients. J. Sens. Actuator Netw. 2018, 7. [Google Scholar] [CrossRef]
  69. Dobbins, C.; Rawassizadeh, R.; Momeni, E. Detecting physical activity within lifelogs towards preventing obesity and aiding ambient assisted living. Neurocomputing 2017, 230, 110–132. [Google Scholar] [CrossRef]
  70. Argent, R.; Slevin, P.; Bevilacqua, A.; Neligan, M.; Daly, A.; Caulfield, B. Clinician perceptions of a prototype wearable exercise biofeedback system for orthopaedic rehabilitation: A qualitative exploration. BMJ Open 2018, 8. [Google Scholar] [CrossRef]
  71. Zhuang, Y.; Song, C.; Wang, A.; Lin, F.; Li, Y.; Gu, C.; Li, C.; Xu, W. SleepSense: Non-invasive sleep event recognition using an electromagnetic probe. In Proceedings of the 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Cambridge, MA, USA, 9–12 June 2015. [Google Scholar] [CrossRef]
  72. Amira, T.; Dan, I.; Az-eddine, B.; Ngo, H.H.; Said, G.; Katarzyna, W. Monitoring chronic disease at home using connected devices. In Proceedings of the 2018 13th Annual Conference on System of Systems Engineering (SoSE), Paris, France, 19–22 June 2018; pp. 400–407. [Google Scholar] [CrossRef]
  73. Cortinas, R.; Gonzaga, J.M.; Green, A.R.; Saulenas, A.M.; BuSha, B.F. TCNJ Athlete Tracker. In Proceedings of the 2015 41st Annual Northeast Biomedical Engineering Conference (NEBEC), Troy, NY, USA, 17–19 April 2015. [Google Scholar] [CrossRef]
  74. Hörmann, T.; Hesse, M.; Adams, M.; Rückert, U. A Software Assistant for User-Centric Calibration of a Wireless Body Sensor. In Proceedings of the 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), San Francisco, CA, USA, 14–17 June 2016; pp. 183–188. [Google Scholar] [CrossRef]
  75. Warmerdam, L.; Riper, H.; Klein, M.; van den Ven, P.; Rocha, A.; Ricardo Henriques, M.; Tousset, E.; Silva, H.; Andersson, G.; Cuijpers, P. Innovative ICT solutions to improve treatment outcomes for depression: the ICT4Depression project. Stud. Health Technol. Inform. 2012, 181, 339–343. [Google Scholar]
  76. Rajagopalan, R.; Litvan, I.; Jung, T.P. Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions. Sensors 2017, 17, 2509. [Google Scholar] [CrossRef]
  77. Tedesco, S.; Barton, J.; O’Flynn, B. A review of activity trackers for senior citizens: Research perspectives, commercial landscape and the role of the insurance industry. Sensors 2017, 17, 1277. [Google Scholar] [CrossRef]
  78. Mehta, L.S.; Beckie, T.M.; DeVon, H.A.; Grines, C.L.; Krumholz, H.M.; Johnson, M.N.; Lindley, K.J.; Vaccarino, V.; Wang, T.Y.; Watson, K.E.; et al. Acute Myocardial Infarction in Women A Scientific Statement From the American Heart Association. Circulation 2016, 133, 916–947. [Google Scholar] [CrossRef] [PubMed]
  79. Li, S.; Fonarow, G.C.; Mukamal, K.J.; Liang, L.; Schulte, P.J.; Smith, E.E.; DeVore, A.; Hernandez, A.F.; Peterson, E.D.; Bhatt, D.L. Sex and Race/Ethnicity–Related Disparities in Care and Outcomes After Hospitalization for Coronary Artery Disease Among Older Adults. Circ. Cardiovasc. Qual. Outcomes 2016, 9, 36–44. [Google Scholar] [CrossRef] [PubMed]
  80. Regitz-Zagrosek, V. Sex and gender differences in health. Science & Society Series on Sex and Science. EMBO Rep. 2012, 13, 596–603. [Google Scholar] [CrossRef] [PubMed]
  81. Smulders, E.; van Lankveld, W.; Laan, R.; Duysens, J.; Weerdesteyn, V. Does osteoporosis predispose falls? a study on obstacle avoidance and balance confidence. BMC Musculoskelet. Disord. 2011, 12, 1. [Google Scholar] [CrossRef]
  82. Scheffer, A.; Schuurmans, M.; van Dijk, N.; van der Hooft, T.; de Rooij, S. Fear of falling: measurement strategy, prevalence, risk factors and consequences among older persons. Age Ageing 2008, 37, 19–24. [Google Scholar] [CrossRef]
  83. Delbaere, K.; Crombez, G.; Vanderstraeten, G.; Willems, T.; Cambier, D. Fear-related avoidance of activities, falls and physical frailty. A prospective community-based cohort study. Age Ageing 2004, 33, 368–373. [Google Scholar] [CrossRef]
  84. Arnold, D.; Busch, A.; Schachter, C.; Harrison, L.; Olsynski, W. The relationship of intrinsic fall risk factors to a recent history of falling in older women with osteoporosis. J. Orthop. Sports Phys. Ther. 2005, 35, 452–460. [Google Scholar] [CrossRef]
  85. Liu-Ambrose, T.; Khan, K.; Donaldson, M.; Eng, J.; Lord, S.; McKay, H. Falls-related self-efficacy is independently associated with balance and mobility in older women with low bone mass. J. Gerontol. Ser. A 2006, 51, 832–838. [Google Scholar] [CrossRef]
  86. A Patient’s Guide to Adult Kyphosis. Available online: https://www.umms.org/ummc/health-services/orthopedics/services/spine/patient-guides/adult-kyphosis (accessed on 16 January 2020).
Figure 1. The article selection process.
Figure 1. The article selection process.
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Figure 2. Number of articles per year. * only the articles published prior to 24 April 2019 are counted.
Figure 2. Number of articles per year. * only the articles published prior to 24 April 2019 are counted.
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Figure 3. Number of authors affiliated in each country. Authors are calculated for each article, i.e., an author may be calculated more than once and in more than one country.
Figure 3. Number of authors affiliated in each country. Authors are calculated for each article, i.e., an author may be calculated more than once and in more than one country.
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Figure 4. Number of articles per country. Papers with several authors may be counted for several countries.
Figure 4. Number of articles per country. Papers with several authors may be counted for several countries.
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Figure 5. Category-wise distribution of the selected articles. Number of articles for Additional = 10, Asthma/COPD = 6, Cardiovascular diseases = 8, Diabetes and nutrition = 5, Gait and fall = 15, Neurological diseases = 8, Physical activity recognition = 7, Rehabilitation = 7, Stress and sleep = 7.
Figure 5. Category-wise distribution of the selected articles. Number of articles for Additional = 10, Asthma/COPD = 6, Cardiovascular diseases = 8, Diabetes and nutrition = 5, Gait and fall = 15, Neurological diseases = 8, Physical activity recognition = 7, Rehabilitation = 7, Stress and sleep = 7.
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Figure 6. Distribution of the number of participants per included study. - denotes studies which did not provide information on number of participants.
Figure 6. Distribution of the number of participants per included study. - denotes studies which did not provide information on number of participants.
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Figure 7. Distribution of the number of participants per article category. - denotes studies which did not provide information on number of participants.
Figure 7. Distribution of the number of participants per article category. - denotes studies which did not provide information on number of participants.
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Table 1. An overview of search phrases and databases used during article retrieval.
Table 1. An overview of search phrases and databases used during article retrieval.
DatabaseSearch PhraseNumber of Articles
Web of Science Core CollectionALL FIELDS: ((“body sensor" or “wireless body sensor” or “wireless wearable technology” or “biomedical sensor” or “IoT”) and (“Ecare” or “mHealth” or “eHealth’) and (“Social impact” or “Compliance” or “Acceptance” or “Clinical trial” or ‘Pilot test” or ‘Human input” or “Feedback” or “Pilot application” or “Human in the loop”))7
Web of Science Core CollectionALL FIELDS:((“body sensor” or “wireless body sensor” or “wireless wearable technology” or “biomedical sensor" or “IoT”) and (“care" or “Health”) and (“Social impact” or “Compliance” or “Acceptance” or “Clinical trial" or “Pilot test” or “Human input” or “Feedback” or “Pilot application" or “Human in the loop”))142
MEDLINE (Web of Science)TOPIC: ((((((“body sensor”) OR “wireless body sensor”) OR “wireless wearable technology”) OR “biomedical sensor”) OR “IoT”) AND (“care”) OR “Health”)) AND ((((((((“Social impact”) OR “Compliance”) OR “Acceptance”) OR “Clinical trial”) OR “Pilot test”) OR “Human input”) OR “Feedback”) OR “Pilot application") OR “Human in the loop")) Timespan: All years. Indexes: MEDLINE.25
ScopusALL(body sensor OR wireless body sensor OR wireless wearable technology OR biomedical sensor) AND (ecare OR mhealth OR ehealth) AND ( Social impact OR compliance OR acceptance OR Clinical trial OR Pilot test ) Limiting to English187
ScienceDirectTitle, abstract, keywords: “wearable sensors” and health and impact. Limited to review articles, research articles, conference abstracts, case reports.13
ScienceDirectTitle, abstract, keywords: “body sensor” and health and impact. Limited to review articles, research articles, conference abstracts, case reports.5
Academic Search EliteFree text search: “body sensor” and health and impact English.8
Academic Search EliteFree text search: “body sensor” and health and acceptance3
ACM Digital Library(+“body sensor” +and +health +and +impact)12
IEEE Xplore“body sensor” and health and impact81
IEEE Xplore“body sensor” and health and trial12
Table 2. List of articles reporting on conducted studies. —indicates that information is missing.
Table 2. List of articles reporting on conducted studies. —indicates that information is missing.
Author, YearRef.Article CategoryResearch DesignNo. of ParticipantsSensor Category
Bonnevie et al. 2019[13]Asthma/COPDObservational104Vital signs
5
Caulfield et al. 2014[14]Asthma/COPDObservational10Physical activity
Estrada et al. 2016[15]Asthma/COPDObservational1Other
Katsaras et al. 2011[16]Asthma/COPDRandomized control48Other
Naranjo-Hernández et al. 2018[17]Asthma/COPDObservational2Vital signs
9
Huang et al. 2014a[18]Cardiovascular diseases-225ECG
Huang et al. 2014b[19]Cardiovascular diseasesCase-control84ECG
Javaid et al. 2018[20]Cardiovascular diseasesObservational60Other
Li et al. 2019[3]Cardiovascular diseasesObservational16Other
Raad et al. 2015[21]Cardiovascular diseases-30ECG
-2
Simjanoska et al. 2018[22]Cardiovascular diseasesObservational16ECG
3
25
7Dataset ECG
Susič and Stanič 2016[23]Cardiovascular diseases-13ECG
Al-Taee et al. 2015[24]Diabetes and nutrition-22Other
Alshurafa et al. 2014 and Alshurafa et al. 2015[25,26]Diabetes and nutritionObservational10Other
20
Dong and Biswas 2017[27]Diabetes and nutritionObservational14Other
Onoue et al. 2017[28]Diabetes and nutritionRandomized control101Physical activity
Atallah 2012[29]Gait and fallObservational34Physical activity
Godfrey et al. 2014[30]Gait and fallObservational24Physical activity
Lee et al. 2015[31]Gait and fallObservational11Physical activity
Liang et al. 2012[32]Gait and fallObservational8Physical activity
Liang et al. 2018[33]Gait and fallObservational18Physical activity
Paiman et al. 2016[34]Gait and fallObservational2Other
Tino et al. 2011[35]Gait and fallObservational3Other
Williams et al. 2015[36]Gait and fallObservational5–6Physical activity
Wu et al. 2013[4]Gait and fallObservational7Physical activity
Wu et al. 2019[37]Gait and fallObservational15Physical activity
Zhao et al. 2012[38]Gait and fallObservational8Physical activity
Zhong et al. 2019[39]Gait and fallObservational56Physical activity
Giuberti et al. 2015[40]Neurological diseasesObservational24Physical activity
Gong et al. 2015, Gong et al. 2016[41,42]Neurological diseasesCase-control41Physical activity
Kuusik et al. 2018[43]Neurological diseasesObservational51Physical activity
Sok et al. 2018[44]Neurological diseasesObservational13Physical activity
Stamate et al. 2017 and Stamate et al. 2018[45,46]Neurological diseasesObservational12Other
Castro et al. 2017 and Rodriguez et al. 2017[5,6]Physical activity recognitionObservational3Other
Doron et al. 2013[7]Physical activity recognitionObservational65Other
20
Rednic et al. 2012[47]Physical activity recognitionObservational17Physical activity
Xu et al. 2014[8]Physical activity recognitionObservational14Other
Xu et al. 2016[9]Physical activity recognitionObservational4Other
3Physical activity
5
6
Argent et al. 2019[48]RehabilitationObservational15Physical activity
Banos et al. 2015[49]RehabilitationObservational10Other
Lee et al. 2018[50]RehabilitationCase-control30Physical activity
Timmermans et al. 2010[51]RehabilitationObservational9Physical activity
Whelan et al. 2017[52]RehabilitationObservational55Physical activity
Xu et al. 2017[53]RehabilitationObservational6Other
Lin et al. 2012[54]Stress and sleepCase-control18 (6/12)Physical activity
Nakamura et al. 2017[55]Stress and sleepObservational4Other
Parnandi and Gutierrez-Osuna 2017[56]Stress and sleepRandomized control25Other
Uday et al. 2018[57]Stress and sleepObservational10Other
Umemura et al. 2017[58]Stress and sleepCase-control54Other
Velicu et al. 2016[10]Stress and sleepObservational--
Ayzenberg and Picard 2014[59]AdditionalCrossover10Other
Pagán et al. 2016[60]AdditionalObservational2Other
Rawasdeh et al. 2017[61]AdditionalObservational55ECG
Seeger et al. 2012[11]Additional--Other
Wannenburg and Malekian 2015[12]AdditionalObservational4–8Vital signs
Wu et al. 2018[62]AdditionalObservational20ECG
Table 3. Demographic information on conducted studies. - indicates that information is missing.
Table 3. Demographic information on conducted studies. - indicates that information is missing.
Ref.Article CategoryNo. of ParticipantsAge GroupAge StatisticsMaleFemalePatientHealthy
[13]Asthma/COPD10457–706467 (64%)37 (36%)104
550–6662--5
[14]Asthma/COPD1061.5 ± 5.75510
[15]Asthma/COPD1--11
[16]Asthma/COPD48--4848
[17]Asthma/COPD236 and 4222
955–7664 ± 6.6 639
[18]Cardiovascular diseases225----225
[19]Cardiovascular diseases84----1 group1 group
[20]Cardiovascular diseases60-26.9 ± 6.1 283260
[3]Cardiovascular diseases16------
[21]Cardiovascular diseases3020–23----
2----2
[22]Cardiovascular diseases1616–72-----
325–27-----
2520–73---1411
720–74---7
[23]Cardiovascular diseases13-50.6 ± 9 8513
[24]Diabetes and nutrition22----22
[25,26]Diabetes and nutrition1020–4082--
2020–40128--
[27]Diabetes and nutrition14--9514
[28]Diabetes and nutrition101-57.1 ± 12.5 5645101
[29]Gait and fall34-28.22 ± 12.77 211334
[30]Gait and fall24 (12/12)20–4032.5 ± 4.8 7512
65.0 ± 8.8 5712
[31]Gait and fall11-27.6 ± 4.3 1111
[32]Gait and fall8-23 ± 3.45 88
[33]Gait and fall18-25 ± 3.24 12618
[34]Gait and fall228 and 24-112
[35]Gait and fall340–70-----
[36]Gait and fall5–6 (1/5)27-1--
21–362741--
[4]Gait and fall7------
[37]Gait and fall1520–27---15
[38]Gait and fall8-28.5 ± 4.3 --8
[39]Gait and fall56 (28/28)-24.6 ± 2.7 141428
>5566.1 ± 5.0 181028
[40]Neurological diseases2431–7965.9 ± 12.3 17724
[41,42]Neurological diseases41 (28/13)-40.5 ± 9.4 25%25%2813
-39.3 ± 10.3 47%53%
[43]Neurological diseases51----51
[44]Neurological diseases1322–50-9413
[45,46]Neurological diseases12----12
[5,6]Physical activity recognition3------
[7]Physical activity recognition65------
20------
[47]Physical activity recognition17--107--
[8]Physical activity recognition14------
[9]Physical activity recognition4------
3----3
5----5
6--33--
[48]Rehabilitation15-63 ± 8.32 6915
[49]Rehabilitation1021–37-82--
[50]Rehabilitation20-54.4 ± 10.1 --20
1053.8 ± 11.4 --10
[51]Rehabilitation9-60.7549
[52]Rehabilitation55-24.21 ± 5.25 371855
[53]Rehabilitation6-72.5 ± 6.0 33--
[54]Stress and sleep18 (6/12)19–22 overall-51--
111--
[55]Stress and sleep425–36-44
[56]Stress and sleep2519–33-1510--
[57]Stress and sleep10----10
[58]Stress and sleep54 (26/28)-22--54
-21----
[10]Stress and sleep-------
[59]Additional1025–3530.8 ± 4.2 9110
[60]Additional2--22
[61]Additional5518–22-50%50%--
[11]Additional-------
[12]Additional4–8 (4/4)------
------
[62]Additional20----20
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