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A Systematic Review of Wearable Sensors and IoT-Based Monitoring Applications for Older Adults – a Focus on Ageing Population and Independent Living

  • Mobile & Wireless Health
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Abstract

This review aims to present current advancements in wearable technologies and IoT-based applications to support independent living. The secondary aim was to investigate the barriers and challenges of wearable sensors and Internet-of-Things (IoT) monitoring solutions for older adults. For this work, we considered falls and activity of daily life (ADLs) for the ageing population (older adults). A total of 327 articles were screened, and 14 articles were selected for this review. This review considered recent studies published between 2015 and 2019. The research articles were selected based on the inclusion and exclusion criteria, and studies that support or present a vision to provide advancement to the current space of ADLs, independent living and supporting the ageing population. Most studies focused on the system aspects of wearable sensors and IoT monitoring solutions including advanced sensors, wireless data collection, communication platform and usability. Moderate to low usability/ user-friendly approach is reported in most of the studies. Other issues found were inaccurate sensors, battery/ power issues, restricting the users within the monitoring area/ space and lack of interoperability. The advancement of wearable technology and the possibilities of using advanced IoT technology to assist older adults with their ADLs and independent living is the subject of many recent research and investigation.

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References

  1. Tinetti, M. E., and Kumar, C., The patient who falls. JAMA 303(3):258–266, 2010.

    Article  CAS  Google Scholar 

  2. Nguyen, H., Mirza, F., Naeem, M. A., and Baig, M. M., Falls management framework for supporting an independent lifestyle for older adults: A systematic review. Aging Clin. Exp. Res.:1–12, 2018.

  3. Nguyen, H., Mirza, F., Naeem, M. A., and Baig, M. M., Detecting falls using a wearable accelerometer motion sensor. In: Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. ACM, 2017, 422–431.

  4. GholamHosseini, H., Baig, M. M., Meintjes, A., Mirza, F., and Lindén, M., Smartphone-based blood pressure monitoring for falls risk assessment: techniques and technologies. In: Human Monitoring, Smart Health and Assisted Living: Techniques and Technologies. Vol. 9, 2017, 203.

  5. Baig, M. M., Gholamhosseini, H., and Connolly, M. J., Falls risk assessment for hospitalised older adults: A combination of motion data and vital signs. Aging Clin. Exp. Res. 28(6):1159–1168, 2016.

    Article  Google Scholar 

  6. Sabesan, S., and Sankar, R., Improving long-term management of epilepsy using a wearable multimodal seizure detection system. Epilepsy Behav. 46:56–57, 2015.

    Article  Google Scholar 

  7. Wan, J., Gu, X., Chen, L., and Wang, J., Internet of things for ambient assisted living: challenges and future opportunities. In: Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2017 International Conference on. IEEE, 2017, 354–357.

  8. Vallabh, P., and Malekian, R., Fall detection monitoring systems: A comprehensive review. J. Ambient. Intell. Humaniz. Comput. 9(6):1809–1833, 2018.

    Article  Google Scholar 

  9. Nguyen, H., Mirza, F., Naeem, M. A., and Baig, M. M., Falls management framework for supporting an independent lifestyle for older adults: A systematic review. Aging Clin. Exp. Res. 30(11):1275–1286, 2018. Journal article.

    Article  Google Scholar 

  10. Baig, M. M., Gholamhosseini, H., and Connolly, M. J., A comprehensive survey of wearable and wireless ECG monitoring systems for older adults. Med. Biol. Eng. Comput. 51(5):485–495, 2013.

    Article  Google Scholar 

  11. Banaee, H., Ahmed, M. U., and Loutfi, A., Data mining for wearable sensors in health monitoring systems: A review of recent trends and challenges. Sensors 13(12):17472–17500, 2013.

    Article  CAS  Google Scholar 

  12. Baig, M. M., and Gholamhosseini, H., Smart health monitoring systems: An overview of design and modeling. J. Med. Syst. 37(2):9898, 2013.

    Article  Google Scholar 

  13. Moher, D., Liberati, A., Tetzlaff, J., and Altman, D. G., Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Ann. Intern. Med. 151(4):264–269, 2009.

    Article  Google Scholar 

  14. Yuan, J., Tan, K. K., Lee, T. H., and Koh, G. C. H., Power-efficient interrupt-driven algorithms for fall detection and classification of activities of daily living. IEEE Sensors J. 15(3):1377–1387, 2015.

    Article  Google Scholar 

  15. Pierleoni, P., Belli, A., Palma, L., Pellegrini, M., Pernini, L., and Valenti, S., A high reliability wearable device for elderly fall detection. IEEE Sensors J. 15(8):4544–4553, 2015.

    Article  CAS  Google Scholar 

  16. Pierleoni, P. et al., A wearable fall detector for elderly people based on AHRS and barometric sensor. IEEE Sensors J. 16(17):6733–6744, 2016.

    Article  Google Scholar 

  17. Zhu, L., Wang, R., Wang, Z., and Yang, H., TagCare: Using RFIDs to monitor the status of the elderly living alone. IEEE Access 5:11364–11373, 2017.

    Article  Google Scholar 

  18. Yacchirema, D., de Puga, J. S., Palau, C., and Esteve, M., Fall detection system for elderly people using IoT and ensemble machine learning algorithm. Pers. Ubiquit. Comput.:1–17, 2019.

  19. Maimoon, L. et al., SilverLink: developing an international smart and connected home monitoring system for senior care. In: International Conference on Smart Health. Springer, 2016, 65–77.

  20. Hegde, N., and Sazonov, E., SmartStep: A fully integrated, low-power insole monitor. Electronics 3(2):381–397, 2014.

    Article  Google Scholar 

  21. Hegde, N., Bries, M., Swibas, T., Melanson, E., and Sazonov, E., Automatic recognition of activities of daily living utilizing insole-based and wrist-worn wearable sensors. IEEE journal of biomedical and health informatics 22(4):979–988, 2018.

    Article  Google Scholar 

  22. Billis, A. S., Papageorgiou, E. I., Frantzidis, C. A., Tsatali, M. S., Tsolaki, A. C., and Bamidis, P. D., A decision-support framework for promoting independent living and ageing well. IEEE journal of biomedical and health informatics 19(1):199–209, 2015.

    Article  Google Scholar 

  23. Tan, T.-H., Gochoo, M., Jean, F.-R., Huang, S.-C., and Kuo, S.-Y., Front-door event classification algorithm for elderly people living alone in smart house using wireless binary sensors. IEEE Access 5:10734–10743, 2017.

    Article  Google Scholar 

  24. Seo, D., Yoo, B., and Ko, H., Data-driven smart home system for elderly people based on web technologies. Cham: Springer International Publishing, 2016, 122–131.

    Google Scholar 

  25. Khojasteh, S. B., Villar, J. R., Chira, C., González, V. M., and de la Cal, E., Improving fall detection using an on-wrist wearable accelerometer. Sensors (Basel, Switzerland) 18(5):1350, 2018.

    Article  Google Scholar 

  26. Bellagente, P. et al., Remote and non-invasive monitoring of elderly in a smart city context. In: Sensors Applications Symposium (SAS), 2018 IEEE. IEEE, 2018, 1–6.

  27. Kheirkhahan, M. et al., A smartwatch-based framework for real-time and online assessment and mobility monitoring. J. Biomed. Inform. 89:29–40, 2019.

    Article  Google Scholar 

  28. Maimoon, L. et al., SilverLink: developing an international smart and connected home monitoring system for senior care. Cham: Springer International Publishing, 2017, 65–77.

    Google Scholar 

  29. Chen, E. T., The internet of things: opportunities, issues, and challenges. In: The Internet of Things in the Modern Business Environment. IGI Global, 2017, 167–187.

  30. Chen, M., Ma, Y., Song, J., Lai, C.-F., and Hu, B., Smart clothing: Connecting human with clouds and big data for sustainable health monitoring. Mobile Networks and Applications 21(5):825–845, 2016.

    Article  Google Scholar 

  31. Etemadi, M., Inan, O. T., Heller, J. A., Hersek, S., Klein, L., and Roy, S., A wearable patch to enable long-term monitoring of environmental, activity and hemodynamics variables. IEEE Transactions on Biomedical Circuits and Systems 10(2):280–288, 2016.

    Article  Google Scholar 

  32. Wu, W., Zhang, H., Pirbhulal, S., Mukhopadhyay, S. C., and Zhang, Y. T., Assessment of biofeedback training for emotion management through wearable textile physiological monitoring system. IEEE Sensors J. 15(12):7087–7095, 2015.

    Article  Google Scholar 

  33. Rault, T., Bouabdallah, A., Challal, Y., and Marin, F., A survey of energy-efficient context recognition systems using wearable sensors for healthcare applications. Pervasive and Mobile Computing 37:23–44, 2017.

    Article  Google Scholar 

  34. Wu, J., Li, H., Cheng, S., and Lin, Z., The promising future of healthcare services: When big data analytics meets wearable technology. Inf. Manag. 53:1020–1033, 2016.

    Article  Google Scholar 

  35. Thomas, S. S., Nathan, V., Zong, C., Soundarapandian, K., Shi, X., and Jafari, R., BioWatch: A noninvasive wrist-based blood pressure monitor that incorporates training techniques for posture and subject variability. IEEE Journal of Biomedical and Health Informatics 20(5):1291–1300, 2016.

    Article  Google Scholar 

  36. Balamurugan, S., Madhukanth, R., Prabhakaran, V., and Shanker, R. G. K., Internet of health: Applying IoT and big data to manage healthcare systems. International Research Journal of Engineering and Technology (IRJET) 310:732–735, 2016.

  37. Ghosh, A. M., Halder, D., and Hossain, S. A., Remote health monitoring system through IoT. In: 2016 International Conference on Informatics, Electronics and Vision (ICIEV). IEEE, 2016, 921–926.

  38. Lee, W., Yoon, H., and Park, K., Smart ECG monitoring patch with built-in R-peak detection for long-term HRV analysis. Ann. Biomed. Eng.:1–10, 2016.

  39. Kyriazakos, S. et al., eWALL: An intelligent caring home environment offering personalized context-aware applications based on advanced sensing. Wirel. Pers. Commun. 87(3):1093–1111, 2016.

    Article  Google Scholar 

  40. Araújo, F. H., Santana, A. M., and Neto, P. d. A. S., Using machine learning to support healthcare professionals in making preauthorisation decisions. Int. J. Med. Inform. 94:1–7, 2016.

    Article  Google Scholar 

  41. Klaassen, B., van Beijnum, B. J., and Hermens, H. J., Usability in telemedicine systems—A literature survey. Int. J. Med. Inform. 93:57–69, 2016.

    Article  CAS  Google Scholar 

  42. Rajput, D. S., and Gour, R., An IoT framework for healthcare monitoring systems. International Journal of Computer Science and Information Security (IJCSIS) 14(5):451, 2016.

  43. Ribeiro, J., Wearable technology spending: a strategic approach to decision-making. In: Wearable Technology and Mobile Innovations for Next-Generation Education, 2016, 37.

  44. Michard, F., A sneak peek into digital innovations and wearable sensors for cardiac monitoring. J. Clin. Monit. Comput.:1–7, 2016.

  45. Iqbal, M. H., Aydin, A., Brunckhorst, O., Dasgupta, P., and Ahmed, K., A review of wearable technology in medicine. J. R. Soc. Med. 109(10):372–380, 2016.

    Article  Google Scholar 

  46. Prakash, R., Ganesh, A. B., and Sivabalan, S., Network coded cooperative communication in a real-time wireless hospital sensor network. J. Med. Syst. 41(5):72, 2017.

    Article  CAS  Google Scholar 

  47. Elsebakhi, E. et al., Large-scale machine learning based on functional networks for biomedical big data with high performance computing platforms. Journal of Computational Science 11:69–81, 2015.

    Article  Google Scholar 

  48. Miller, R. A., Diagnostic decision support systems. In: Clinical Decision Support Systems. Springer, 2016, 181–208.

  49. Berner, E. S., and La Lande, T. J., Overview of clinical decision support systems. In: Clinical Decision Support Systems. Springer, 2016, 1–17.

  50. Wright, A. et al., Analysis of clinical decision support system malfunctions: A case series and survey. Journal of the American Medical Informatics Association 23(6):1068–1076, 2016. https://doi.org/10.1093/jamia/ocw005.

    Article  Google Scholar 

  51. Baig, M. M., Hosseini, H. G., and Lindén, M., Machine learning-based clinical decision support system for early diagnosis from real-time physiological data. In: Region 10 Conference (TENCON), 2016 IEEE. IEEE, 2016, 2943–2946.

  52. Price-Haywood, E. G., Harden-Barrios, J., Ulep, R., and Luo, Q., eHealth literacy: Patient engagement in identifying strategies to encourage use of patient portals among older adults. Population Health Management 20:486–494, 2017.

    Article  Google Scholar 

  53. Davis, S., Roudsari, A., Raworth, R., Courtney, K. L., and MacKay, L., Shared decision-making using personal health record technology: A scoping review at the crossroads. J. Am. Med. Inform. Assoc. 24:857–866, 2017.

    Article  Google Scholar 

  54. Milani, R. V., and Franklin, N. C., The role of technology in healthy living medicine. Prog. Cardiovasc. Dis. 59:487–491, 2017.

    Article  Google Scholar 

  55. Park, E., Park, E., Kim, K. J., Kim, K. J., Kwon, S. J., and Kwon, S. J., Understanding the emergence of wearable devices as next-generation tools for health communication. Inf. Technol. People 29(4):717–732, 2016.

    Article  Google Scholar 

  56. Rupp, M. A., Michaelis, J. R., McConnell, D. S., and Smither, J. A., The impact of technological trust and self-determined motivation on intentions to use wearable fitness technology. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting. Vol. 60, no. 1. SAGE Publications, 2016, 1434–1438.

  57. Ullah, F., Habib, M. A., Farhan, M., Khalid, S., Durrani, M. Y., and Jabbar, S., Semantic interoperability for big-data in heterogeneous IoT infrastructure for healthcare. Sustain. Cities Soc. 34:90–96, 2017.

    Article  Google Scholar 

  58. Kovacs, E., Bauer, M., Kim, J., Yun, J., Le Gall, F., and Zhao, M., Standards-based worldwide semantic interoperability for IoT. IEEE Commun. Mag. 54(12):40–46, 2016.

    Article  Google Scholar 

  59. Wu, A. Y., and Munteanu, C., Understanding older users’ acceptance of wearable interfaces for sensor-based fall risk assessment. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 2018, 119.

  60. Simblett, S. et al., Barriers to and facilitators of engagement with remote measurement technology for managing health: Systematic review and content analysis of findings. J. Med. Internet Res. 20(7):e10480, 2018.

    Article  Google Scholar 

  61. Ahmadi, H., Arji, G., Shahmoradi, L., Safdari, R., Nilashi, M., and Alizadeh, M., The application of internet of things in healthcare: A systematic literature review and classification. Univ. Access Inf. Soc.:1–33, 2018.

  62. Spanakis, E. G., Psaraki, M., and Sakkalis, V., Congestive heart failure risk assessment monitoring through internet of things and mobile personal health systems. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018, 2925–2928.

  63. Malwade, S. et al., Mobile and wearable technologies in healthcare for the ageing population. Comput. Methods Prog. Biomed. 161:233–237, 2018.

    Article  Google Scholar 

  64. Yang, Z., Zhou, Q., Lei, L., Zheng, K., and Xiang, W., An IoT-cloud based wearable ECG monitoring system for smart healthcare. J. Med. Syst. 40(12):286, 2016.

    Article  Google Scholar 

  65. Raja, K., Saravanan, S., Anitha, R., Priya, S. S., and Subhashini, R., Design of a low power ECG signal processor for wearable health system-review and implementation issues. In: Intelligent Systems and Control (ISCO), 2017 11th International Conference on. IEEE, 2017, 383–387.

  66. Kumari, P., Mathew, L., and Syal, P., Increasing trend of wearables and multimodal interface for human activity monitoring: A review. Biosens. Bioelectron. 90:298–307, 2017.

    Article  CAS  Google Scholar 

  67. Kurien, M., Trott, N., and Sanders, D., Long-term care for patients with coeliac disease in the UK: A review of the literature and future directions. J. Hum. Nutr. Diet. 29:617–623, 2016.

    Article  CAS  Google Scholar 

  68. Jolicoeur, M., Novel Vitality Indices Derived From the Hexoskin in Patients Affected With Angina Undergoing Coronary Revascularization or Medical Therapy (NOVA-SKIN) [Cinical Trial]. 2016, 15 October 2016. Available: https://clinicaltrials.gov/ct2/show/NCT02591758?term=hexoskin&rank=1.

  69. C. T. i. (Hexoskin). Key Metrics delivered by Hexoskin. 2016. Available: http://www.hexoskin.com/pages/key-metrics-delivered-by-hexoskin.

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  1. School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland, 1142, New Zealand

    Mirza Mansoor Baig, Shereen Afifi, Hamid GholamHosseini & Farhaan Mirza

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  1. Mirza Mansoor Baig
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Baig, M.M., Afifi, S., GholamHosseini, H. et al. A Systematic Review of Wearable Sensors and IoT-Based Monitoring Applications for Older Adults – a Focus on Ageing Population and Independent Living. J Med Syst 43, 233 (2019). https://doi.org/10.1007/s10916-019-1365-7

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