A Systematic Review of Wearable Sensors for Monitoring Physical Activity

by | Jan 12, 2022 | Research Publications

Author(s):

  • Annica Kristoffersson
  • Maria Lindén

Abstract:

his article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological disease progression. The article provides in-depth information on the retrieved articles and discusses study shortcomings related to demographic factors, i.e., age, gender, healthy participants vs patients, and study conditions. It is well known that motion patterns change with age and the onset of illnesses, and that the risk of falling increases with age. Yet, studies including older persons are rare. Gender distribution was not even provided in several studies, and others included only, or a majority of, men. Another shortcoming is that none of the studies were conducted in real-life conditions. Hence, there is still important work to be done in order to increase the usefulness of wearable sensors in these areas. The article highlights flaws in how studies based on previously collected datasets report on study samples and the data collected, which makes the validity and generalizability of those studies low. Exceptions exist, such as the promising recently reported open dataset FallAllD, wherein a longitudinal study with older adults is ongoing.

Documentation:

https://doi.org/10.3390/s22020573

References:

  1. World Health Organization. Neurological Disorders: Public Health Challenges; World Health Organization: Geneva, Switzerland, 2006. [Google Scholar]
  2. Kristoffersson, A.; Lindén, M. A systematic review on the use of wearable body sensors for health monitoring: A qualitative synthesis. Sensors 2020, 20, 1502. [Google Scholar] [CrossRef]
  3. 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]
  4. 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]
  5. 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, 1–8. [Google Scholar] [CrossRef] [PubMed]
  6. 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]
  7. 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]
  8. 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]
  9. 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 19 August 2021).
  10. 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. Intern. Med. 2009, 151, 264–269. [Google Scholar] [CrossRef]
  11. Kristoffersson, A.; Lindén, M. Wearable sensors for monitoring and preventing noncommunicable diseases: A systematic review. Information 2020, 11, 521. [Google Scholar] [CrossRef]
  12. Boutellaa, E.; Kerdjidj, O.; Ghanem, K. Covariance matrix based fall detection from multiple wearable sensors. J. Biomed. Inform. 2019, 94, 103189. [Google Scholar] [CrossRef] [PubMed]
  13. Ramachandran, A.; Adarsh, R.; Pahwa, P.; Anupama, K.R. 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]
  14. Rokni, S.A.; Ghasemzadeh, H. Share-n-Learn: A Framework for Sharing Activity Recognition Models in Wearable Systems with Context-Varying Sensors. ACM Trans. Des. Autom. Electron. Syst. 2019, 24, 39. [Google Scholar] [CrossRef]
  15. Awais, M.; Raza, M.; Ali, K.; Ali, Z.; Irfan, M.; Chughtai, O.; Khan, I.; Kim, S.; Rehman, M.U. An Internet of Things Based Bed-Egress Alerting Paradigm Using Wearable Sensors in Elderly Care Environment. Sensors 2019, 19, 2498. [Google Scholar] [CrossRef]
  16. 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]
  17. Ojetola, O.; Gaura, E.; Brusey, J. Data Set for Fall Events and Daily Activities from Inertial Sensors. In Proceedings of the 6th ACM Multimedia Systems Conference, Portland, OR, USA, 18–20 March 2015; ACM: New York, NY, USA, 2015. MMSys ’15. pp. 243–248. [Google Scholar] [CrossRef]
  18. Frank, K.; Diaz, E.M.; Robertson, P.; Sánchez, F.J.F. Bayesian recognition of safety relevant motion activities with inertial sensors and barometer. In Proceedings of the 2014 IEEE/ION Position, Location and Navigation Symposium—PLANS 2014, Monterey, CA, USA, 5–8 May 2014; pp. 174–184. [Google Scholar] [CrossRef]
  19. Casilari, E.; Santoyo-Ramón, J.A.; Cano-García, J.M. Analysis of a smartphone-based architecture with multiple mobility sensors for fall detection. PLoS ONE 2016, 11, e0168069. [Google Scholar]
  20. Altun, K.; Barshan, B. Human Activity Recognition Using Inertial/Magnetic Sensor Units. In Human Behavior Understanding; Salah, A.A., Gevers, T., Sebe, N., Vinciarelli, A., Eds.; Springer: Berlin/Heidelberg, Germany, 2010; pp. 38–51. [Google Scholar]
  21. Roggen, D.; Calatroni, A.; Rossi, M.; Holleczek, T.; Förster, K.; Tröster, G.; Lukowicz, P.; Bannach, D.; Pirkl, G.; Ferscha, A.; et al. Collecting complex activity datasets in highly rich networked sensor environments. In Proceedings of the 2010 Seventh International Conference on Networked Sensing Systems (INSS), Kassel, Germany, 15–18 June 2010; pp. 233–240. [Google Scholar] [CrossRef]
  22. Ghasemzadeh, H.; Amini, N.; Saeedi, R.; Sarrafzadeh, M. Power-Aware Computing in Wearable Sensor Networks: An Optimal Feature Selection. IEEE Trans. Mob. Comput. 2015, 14, 800–812. [Google Scholar] [CrossRef]
  23. Micucci, D.; Mobilio, M.; Napoletano, P. UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones. Appl. Sci. 2017, 7, 1101. [Google Scholar] [CrossRef]
  24. Wickramasinghe, A.; Ranasinghe, D.C.; Fumeaux, C.; Hill, K.D.; Visvanathan, R. Sequence Learning with Passive RFID Sensors for Real-Time Bed-Egress Recognition in Older People. IEEE J. Biomed. Health Inform. 2017, 21, 917–929. [Google Scholar] [CrossRef]
  25. Reiss, A.; Stricker, D. Creating and Benchmarking a New Dataset for Physical Activity Monitoring. In Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments, Heraklion, Crete, Greece, 5–7 June 2012; ACM: New York, NY, USA, 2012. PETRA ’12. pp. 40:1–40:8. [Google Scholar] [CrossRef]
  26. Casale, P.; Pujol, O.; Radeva, P. Personalization and user verification in wearable systems using biometric walking patterns. Pers. Ubiquitous Comput. 2012, 16, 563–580. [Google Scholar] [CrossRef]
  27. Saleh, M.; Abbas, M.; Le Jeannès, R.B. FallAllD: An Open Dataset of Human Falls and Activities of Daily Living for Classical and Deep Learning Applications. IEEE Sens. J. 2021, 21, 1849–1858. [Google Scholar] [CrossRef]
  28. Zhang, Z. Microsoft Kinect Sensor and Its Effect. IEEE Multimed. 2012, 19, 4–12. [Google Scholar] [CrossRef]
  29. World Health Organization. Falls. Available online: https://www.who.int/news-room/fact-sheets/detail/falls (accessed on 4 May 2020).
  30. Scheffer, A.C.; Schuurmans, M.J.; van Dijk, N.; van der Hooft, T.; de Rooij, S.E. Fear of falling: Measurement strategy, prevalence, risk factors and consequences among older persons. Age Ageing 2008, 37, 19–24. [Google Scholar] [CrossRef]
  31. 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] [PubMed]
  32. 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]
  33. 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, 1093514. [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, 11. [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 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, 30 August–3 September 2011; pp. 5203–5206. [Google Scholar] [CrossRef]
  36. Williams, B.; Allen, B.; True, H.; Fell, N.; Levine, D.; Sartipi, M. 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, Cambridge, MA, USA, 9–12 June 2015. [Google Scholar]
  37. Najafi, B.; Aminian, K.; Paraschiv-Ionescu, A.; Loew, F.; Bula, C.J.; Robert, P. Ambulatory system for human motion analysis using a kinematic sensor: Monitoring of daily physical activity in the elderly. IEEE Trans. Biomed. Eng. 2003, 50, 711–723. [Google Scholar] [CrossRef]
  38. McCamley, J.; Donati, M.; Grimpampi, E.; Mazza, C. An enhanced estimate of initial contact and final contact instants of time using lower trunk inertial sensor data. Gait Posture 2012, 36, 316–318. [Google Scholar] [CrossRef]
  39. Zijlstra, W.; Hof, A.L. Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait Posture 2003, 18, 1–10. [Google Scholar] [CrossRef]
  40. Menz, H.B.; Latt, M.D.; Tiedemann, A.; Mun San Kwan, M.; Lord, S.R. Reliability of the GAITRite walkway system for the quantification of temporo-spatial parameters of gait in young and older people. Gait Posture 2004, 20, 20–25. [Google Scholar] [CrossRef]
  41. Lord, S.; Galna, B.; Verghese, J.; Coleman, S.; Burn, D.; Rochester, L. Independent domains of gait in older adults and associated motor and nonmotor attributes: Validation of a factor analysis approach. J. Gerontol. Ser. Biol. Sci. Med. Sci. 2013, 68, 820–827. [Google Scholar] [CrossRef]
  42. 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, 17–21 July 2018; pp. 4401–4405. [Google Scholar] [CrossRef]
  43. Fakhrulddin, S.S.; Gharghan, S.K. An autonomous wireless health monitoring system based on heartbeat and accelerometer sensors. J. Sens. Actuator Netw. 2019, 8, 39. [Google Scholar] [CrossRef]
  44. 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]
  45. Saleh, M.; Georgi, N.; Abbas, M.; Le Bouqinne Jeannès, R. A Highly Reliable Wrist-Worn Acceleration-Based Fall Detector. In Proceedings of the 2019 27th European Signal Processing Conference (EUSIPCO), A Coruña, Spain, 2–6 September 2019; pp. 1–5. [Google Scholar] [CrossRef]
  46. 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]
  47. 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, 5–7 January 2012; pp. 763–766. [Google Scholar] [CrossRef]
  48. 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]
  49. 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, United Arab Emirates, 19–21 October 2015; pp. 238–245. [Google Scholar] [CrossRef]
  50. 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]
  51. Ramachandran, A.; Ramesh, A.; Pahwa, P.; Atreyaa, A.P.; Murari, S.; Anupama, K.R. Performance Analysis of Machine Learning Algorithms for Fall Detection. In Proceedings of the 2019 IEEE International Conference on E-health Networking, Application Services (HealthCom), Bogota, Colombia, 14–16 October 2019; pp. 1–6. [Google Scholar] [CrossRef]
  52. Ramachandran, A.; Ramesh, A.; Karuppiah, A. Evaluation of Feature Engineering on Wearable Sensor-based Fall Detection. In Proceedings of the 2020 International Conference on Information Networking (ICOIN), Barcelona, Spain, 7–10 January 2020; pp. 110–114. [Google Scholar] [CrossRef]
  53. Kristoffersson, A.; Du, J.; Ehn, M. Performance and Characteristics of Wearable Sensor Systems Discriminating and Classifying Older Adults According to Fall Risk: A Systematic Review. Sensors 2021, 21, 5863. [Google Scholar] [CrossRef] [PubMed]
  54. 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]
  55. Rodriguez, C.; Castro, D.M.; Coral, W.; Cabra, J.L.; Velasquez, N.; Colorado, J.; Mendez, D.; Trujillo, L.C. 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]
  56. 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, 5–7 January 2012; pp. 200–203. [Google Scholar] [CrossRef]
  57. Laamarti, F.; Badawi, H.F.; Ding, Y.; Arafsha, F.; Hafidh, B.; Saddik, A.E. An ISO/IEEE 11073 Standardized Digital Twin Framework for Health and Well-Being in Smart Cities. IEEE Access 2020, 8, 105950–105961. [Google Scholar] [CrossRef]
  58. Caya, M.V.C.; Yumang, A.N.; Arai, J.V.; Niñofranco, J.D.A.; Yap, K.A.S. Human Activity Recognition Based on Accelerometer Vibrations Using Artificial Neural Network. In Proceedings of the 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Laoag, Philippines, 29 November–1 December 2019; pp. 1–5. [Google Scholar] [CrossRef]
  59. 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]
  60. 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]
  61. Xu, J.Y.; Wang, Y.; Barrett, M.; Dobkin, B.; Pottie, G.J.; Kaiser, W.J. Personalized multilayer daily 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]
  62. Culman, C.; Aminikhanghahi, S.; Cook, D.J. Easing power consumption of wearable activity monitoring with change point detection. Sensors 2020, 20, 310. [Google Scholar] [CrossRef]
  63. Awais, M.; Chiari, L.; Ihlen, E.A.F.; Helbostad, J.L.; Palmerini, L. Physical Activity Classification for Elderly People in Free-Living Conditions. IEEE J. Biomed. Health Inform. 2019, 23, 197–207. [Google Scholar] [CrossRef] [PubMed]
  64. 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, e026326. [Google Scholar] [CrossRef] [PubMed]
  65. 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] [PubMed]
  66. 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, 1–11. [Google Scholar] [CrossRef] [PubMed]
  67. Kim, Y.; Jung, H.T.; Park, J.; Kim, Y.; Ramasarma, N.; Bonato, P.; Choe, E.K.; Lee, S.I. Towards the Design of a Ring Sensor-based mHealth System to Achieve Optimal Motor Function in Stroke Survivors. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2019, 3, 138. [Google Scholar] [CrossRef]
  68. Liu, X.; Rajan, S.; Ramasarma, N.; Bonato, P.; Lee, S.I. The Use of a Finger-Worn Accelerometer for Monitoring of Hand Use in Ambulatory Settings. IEEE J. Biomed. Health Inform. 2019, 23, 599–606. [Google Scholar] [CrossRef] [PubMed]
  69. 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]
  70. Bisio, I.; Garibotto, C.; Lavagetto, F.; Sciarrone, A. When ehealth meets IOT: A smart wireless system for post-stroke home rehabilitation. IEEE Wirel. Commun. 2019, 26, 24–29. [Google Scholar] [CrossRef]
  71. 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]
  72. 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]
  73. Xu, J.K.; Lee, U.H.; Bao, T.; Huang, Y.J.; Sienko, K.H.; Shull, P.B. 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]
  74. Sanford, J.; Moreland, J.; Swanson, L.; Stratford, P.; Gowland, C. Reliability of the Fugl-Meyer assessment for testing motor performance in patients following stroke. Phys. Ther. 1993, 73, 447–454. [Google Scholar] [CrossRef]
  75. Hayward, K.S.; Eng, J.J.; Boyd, L.A.; Lakhani, B.; Bernhardt, J.; Lang, E. Exploring the role of accelerometers in the measurement of real world upper-limb use after stroke. Brain Impair. 2016, 17, 16–33. [Google Scholar] [CrossRef]
  76. Hallam, J. Haptic mirror therapy glove: Aiding the treatment of a paretic limb after a stroke. In Adjunct 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Proceedings of the 2015 ACM International Symposium on Wearable Computers, Osaka Japan, 7–11 September 2015; ACM: Osaka, Japan, 2015; pp. 459–464. [Google Scholar] [CrossRef]
  77. Friedman, N.; Rowe, J.B.; Reinkensmeyer, D.J.; Bachman, M. The manumeter: A wearable device for monitoring daily use of the wrist and fingers. IEEE J. Biomed. Health Inform. 2014, 18, 1804–1812. [Google Scholar] [CrossRef] [PubMed]
  78. Bergmann, J.; McGregor, A. Body-worn sensor design: What do patients and clinicians want? Ann. Biomed. Eng. 2011, 39, 2299–2312. [Google Scholar] [CrossRef]
  79. Lee, S.I.; Liu, X.; Rajan, S.; Ramasarma, N.; Choe, E.K.; Bonato, P. A novel upper-limb function measure derived from finger-worn sensor data collected in a free-living setting. PLoS ONE 2019, 14, e0212484. [Google Scholar] [CrossRef] [PubMed]
  80. Wolf, S.L.; McJunkin, J.P.; Swanson, M.L.; Weiss, P.S. Pilot normative database for the wolf motor function test. Arch. Phys. Med. Rehabil. 2006, 87, 443–445. [Google Scholar] [CrossRef] [PubMed]
  81. Willmann, R.D.; Lanfermann, G.; Saini, P.; Timmermans, A.; te Vrugt, J.; Winter, S. Home Stroke Rehabilitation for the Upper Limbs. In Proceedings of the 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, 23–26 August 2007; pp. 4015–4018. [Google Scholar] [CrossRef]
  82. Timmermans, A.A.; Lemmens, R.J.; Monfrance, M.; Geers, R.P.; Bakx, W.; Smeets, R.J.; Seelen, H.A. Effects of task-oriented robot training on arm function, activity, and quality of life in chronic stroke patients: A randomized controlled trial. J. Neuroeng. Rehabil. 2014, 11, 1–12. [Google Scholar] [CrossRef]
  83. Madgwick, S.O.H.; Harrison, A.J.L.; Vaidyanathan, R. Estimation of IMU and MARG orientation using a gradient descent algorithm. In Proceedings of the 2011 IEEE International Conference on Rehabilitation Robotics, Zurich, Switzerland, 29 June–1 July 2011; pp. 1–7. [Google Scholar] [CrossRef]
  84. Biering-Sorensen, F. Physical measurements as risk indicators for low-back trouble over a one-year period. Spine 1984, 9, 106–119. [Google Scholar] [CrossRef]
  85. Moffroid, M. Endurance of trunk muscles in persons with chronic low back pain: Assessment, performance, training. J. Rehabil. Res. Dev. 1997, 34, 440–447. [Google Scholar]
  86. Evans, K.; Refshauge, K.; Adams, R. Trunk muscle endurance tests: Reliability, and gender differences in athletes. J. Sci. Med. Sport 2007, 10, 447–455. [Google Scholar] [CrossRef]
  87. Baraka, A.; Shaban, H.; Abou El-Nasr, M.; Attallah, O. Wearable Accelerometer and sEMG-Based Upper Limb BSN for Tele-Rehabilitation. Appl. Sci. 2019, 9, 2795. [Google Scholar] [CrossRef]
  88. 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]
  89. 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]
  90. 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]
  91. 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; pp. 1–6. [Google Scholar] [CrossRef]
  92. 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]
  93. 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, 5–8 February 2018; pp. 201–206. [Google Scholar] [CrossRef]
  94. 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, 14. [Google Scholar] [CrossRef]
  95. 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]
  96. Loconsole, C.; Cascarano, G.; Brunetti, A.; Trotta, G.; Losavio, G.; Bevilacqua, V.; Sciascio, E. A model-free technique based on computer vision and sEMG for classification in Parkinson’s disease by using computer-assisted handwriting analysis. Pattern Recognit. Lett. 2019, 121, 28–36. [Google Scholar] [CrossRef]
  97. Rissanen, S.; Kankaanpää, M.; Meigal, A.; Tarvainen, M.; Nuutinen, J.; Tarkka, I.; Airaksinen, O.; Karjalainen, P. Surface EMG and acceleration signals in Parkinson’s disease: Feature extraction and cluster analysis. Med. Biol. Eng. Comput. 2008, 46, 849–858. [Google Scholar] [CrossRef] [PubMed]
  98. Giuberti, M.; Ferrari, G.; Contin, L.; Cimolin, V.; Azzaro, C.; Albani, G.; Mauro, A. Assigning UPDRS scores in the leg agility task of Parkinsonians: Can it be done through BSN-based kinematic variables? IEEE Internet Things J. 2015, 2, 41–51. [Google Scholar] [CrossRef]
  99. Nolte, G.; Ziehe, A.; Nikulin, V.V.; Schlögl, A.; Krämer, N.; Brismar, T.; Müller, K.R. Robustly Estimating the Flow Direction of Information in Complex Physical Systems. Phys. Rev. Lett. 2008, 100, 234101. [Google Scholar] [CrossRef]
  100. 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. [Google Scholar] [CrossRef]
  101. Lim, C.G.; Tsai, C.Y.; Chen, M.Y. MuscleSense: Exploring Weight Sensing Using Wearable Surface Electromyography (SEMG). In Proceedings of the Fourteenth International Conference on Tangible, Embedded, and Embodied Interaction, Sydney, Australia, 9–12 February 2020. [Google Scholar] [CrossRef]
  102. Paay, J.; Kjeldskov, J.; Sorensen, F.; Jensen, T.; Tirosh, O. Weight-Mate: Adaptive Training Support for Weight Lifting. In Proceedings of the 31st Australian Conference on Human-Computer-Interaction, Fremantle, Australia, 2–5 December 2019. [Google Scholar] [CrossRef]
  103. 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]
  104. Jovanov, E.; Wright, S.; Ganegoda, H. Development of an Automated 30 Second Chair Stand Test Using Smartwatch Application. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Berlin, Germany, 23–27 July 2019; pp. 2474–2477. [Google Scholar] [CrossRef]
  105. Jacob, S.; Alagirisamy, M.; Menon, V.G.; Kumar, B.M.; Jhanjhi, N.Z.; Ponnusamy, V.; Shynu, P.G.; Balasubramanian, V. An Adaptive and Flexible Brain Energized Full Body Exoskeleton With IoT Edge for Assisting the Paralyzed Patients. IEEE Access 2020, 8, 100721–100731. [Google Scholar] [CrossRef]
  106. 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; ACM: New York, NY, USA, 2016. GECCO ’16. pp. 973–980. [Google Scholar] [CrossRef]