Context-Aware AI Systems for Elderly Care Monitoring
DOI:
https://doi.org/10.63345/Keywords:
Elderly care, context-aware AI, healthcare monitoring, activity recognition, fall detection, personalized healthcareAbstract
The world is experiencing an unprecedented demographic shift toward aging populations, with the proportion of elderly individuals increasing steadily across developed and developing nations. This demographic transition has created a pressing need for innovative healthcare solutions that can support independent living, ensure safety, and improve the quality of life of senior citizens. Traditional caregiving models, heavily reliant on human caregivers and hospital-based interventions, are becoming increasingly unsustainable due to rising healthcare costs, caregiver shortages, and the growing demand for round-the-clock monitoring.
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• Alam, M. R., Reaz, M. B. I., & Ali, M. A. M. (2012). A review of smart homes—Past, present, and future. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 1190–1203. https://doi.org/10.1109/TSMCC.2012.2189204
• Alemdar, H., & Ersoy, C. (2010). Wireless sensor networks for healthcare: A survey. Computer Networks, 54(15), 2688–2710. https://doi.org/10.1016/j.comnet.2010.05.003
• Arcelus, A., Jones, M. H., Goubran, R., & Knoefel, F. (2009). Integration of smart home technologies in a health monitoring system for the elderly. Proceedings of the IEEE, 97(7), 1292–1303. https://doi.org/10.1109/JPROC.2009.2016180
• Chen, L., Hoey, J., Nugent, C. D., Cook, D. J., & Yu, Z. (2012). Sensor-based activity recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 790–808. https://doi.org/10.1109/TSMCC.2012.2198883
• Cook, D. J., & Krishnan, N. C. (2015). Activity learning: Discovering, recognizing, and predicting human behavior from sensor data. John Wiley & Sons.
• Demiris, G., & Hensel, B. K. (2008). Technologies for an aging society: A systematic review of “smart home” applications. IMIA Yearbook of Medical Informatics, 17(1), 33–40. https://doi.org/10.1055/s-0038-1638580
• Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z
• Hassan, M. M., Zawoad, S., & Alhadhrami, A. (2021). Context-aware healthcare IoT systems for elderly monitoring: A review. Sensors, 21(7), 2345. https://doi.org/10.3390/s21072345
• Hong, J., Suh, E. H., Kim, J., & Kim, S. (2009). Context-aware systems: A literature review and classification. Expert Systems with Applications, 36(4), 8509–8522. https://doi.org/10.1016/j.eswa.2008.10.071
• Kifle, H., Chin, J. Y., & Miri, A. (2020). Federated learning for healthcare: Systematic review and architecture proposal. IEEE Access, 8, 141069–141089. https://doi.org/10.1109/ACCESS.2020.3012932
• Ko, L. W., & Chang, Y. H. (2020). Emotion recognition and elderly healthcare using wearable sensors: A review. Sensors, 20(22), 6340. https://doi.org/10.3390/s20226340
• Ni, Q., García Hernando, A. B., & de la Cruz, I. P. (2015). The elderly’s independent living in smart homes: A characterization of activities and sensing infrastructure survey to facilitate services development. Sensors, 15(5), 11312–11362. https://doi.org/10.3390/s150511312
• Rashidi, P., & Mihailidis, A. (2013). A survey on ambient-assisted living tools for older adults. IEEE Journal of Biomedical and Health Informatics, 17(3), 579–590. https://doi.org/10.1109/JBHI.2012.2234129
• Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., ... & Cardoso, M. J. (2020). The future of digital health with federated learning. npj Digital Medicine, 3(1), 1–7. https://doi.org/10.1038/s41746-020-00323-1
• Rumi, N. A., Rahman, M. M., & Alam, M. S. (2021). Fall detection systems for elderly people: A systematic review. IEEE Access, 9, 33675–33695. https://doi.org/10.1109/ACCESS.2021.3059750
• Shany, T., Redmond, S. J., Narayanan, M. R., Lovell, N. H., & Marschollek, M. (2012). Sensors-based wearable systems for monitoring of human movement and falls. IEEE Sensors Journal, 12(3), 658–670. https://doi.org/10.1109/JSEN.2011.2141979
• Siuly, S., Li, Y., & Zhang, Y. (2016). EEG-based intelligent systems for healthcare: A review. Applied Sciences, 6(6), 163. https://doi.org/10.3390/app6060163
• Steele, R., Lo, A., Secombe, C., & Wong, Y. K. (2009). Elderly persons’ perception and acceptance of using wireless sensor networks to assist healthcare. International Journal of Medical Informatics, 78(12), 788–801. https://doi.org/10.1016/j.ijmedinf.2009.08.001
• World Health Organization. (2015). World report on ageing and health. WHO Press. https://apps.who.int/iris/handle/10665/186463
• Zheng, Y., Ding, X., Poon, C. C. Y., Lo, B. P. L., Zhang, H., Zhou, X., ... & Zhang, Y. T. (2014). Unobtrusive sensing and wearable devices for health informatics. IEEE Transactions on Biomedical Engineering, 61(5), 1538–1554. https://doi.org/10.1109/TBME.2014.2309951
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