Abstract
In recent years, the health and safety problems of the elderly increase continuously, coupled with the age of information technology, the elderly are difficult to adapt to the society, so the use of modern Internet technology to protect the health and personal safety of the elderly, has become a top priority. Therefore, this paper, based on the Internet of Things technology, mainly monitors the elderly’s indoor behavior, supplemented by the monitoring of physiological indicators, outdoor behavioral trajectories and falls, proposes the Internet of Things health pension scheme design based on the physiological and behavioral indicators of the elderly. This scheme involved the minimum confidence interval solution strategy, according to the old people in different parts of the activity rate for the elderly indoor dwell time detection, and combined with travel anomaly detection algorithm, pulse wave signal analysis algorithm, fall detection algorithm and other algorithms, real-time monitoring of physiological indexes of elders trajectory data and behavior, the guardian and the hospital can check at any time, once the old man has an accident. The system will send abnormal information to the monitoring system of WeChat mini program guardian and community hospital in time, and the corresponding personnel will immediately take treatment measures to ensure the health and safety of the elderly. This system combines the health and safety problems of the elderly to consider the possible accidents, from monitoring, prevention and treatment, the elderly, children and the hospital are closely linked together, to ensure the health and safety of the elderly to provide a comprehensive solution.
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References
Zhou, M., Sun, X., Huang, L.: Chronic disease and medical spending of Chinese elderly in rural region. Int. J. Qual. Health Care 33(1), mzaa142 (2020)
Kim, M., Chang, M., Nam, E., et al.: Fall characteristics among elderly populations in urban and rural areas in Korea. Medicine 99, e23106 (2020)
Sun, X., Yan, W., Zhou, H., et al.: Internet use and need for digital health technology among the elderly: a cross-sectional survey in China. BMC Public Health 20(1) (2020). Article number: 1386. https://doi.org/10.1186/s12889-020-09448-0
Yuan, Y., Liu, Y., Gong, L., et al.: Demand analysis of telenursing for community-dwelling empty-nest elderly based on the Kano model. Telemed. e-Health 27(4), 414–421 (2020)
Kyriakoulis, K., Kollias, A., Destounis, A., et al.: Detection of atrial fibrillation during routine 24-hour ambulatory blood pressure monitoring in the elderly: comparison with 24-hour electrocardiography. J. Hypertens. 36, 109–110 (2018)
Shi, Y., Yang, J., et al.: Research on wireless ecg monitoring system based on IoT technique. Basic Clin. Pharmacol. Toxicol. 118(1), 81–81 (2016)
Mrozek, D., Koczur, A., Maysiak-Mrozek, B.: Fall detection in older adults with mobile IoT devices and machine learning in the cloud and on the edge. Inf. Sci. 537(5), 132–147 (2020)
Boukhennoufa, I., Amira, A., Bensaali, F., Esfahani, S.S.: A novel gateway-based solution for remote elderly monitoring. J. Biomed. Inform. 109, 103521 (2020)
Juarez, J.M., Ochotorena, J.M., Campos, M., et al.: Spatiotemporal data visualisation for homecare monitoring of elderly people. Artif. Intell. Med. 65(2), 97–111 (2015)
Lupiani, E., Juarez, J.M., et al.: Monitoring elderly people at home with temporal case-based reasoning. Knowl.-Based Syst. 134, 116–134 (2017)
Obayashi, K., Masuyama, S.: Pilot and feasibility study on elderly support services using communicative robots and monitoring sensors integrated with cloud robotics. Clin. Ther. 42(2), 364–371 (2020)
Cappelleri, C., Janoschka, A., Berli, R., et al.: Twenty-four-hour ambulatory blood pressure monitoring in very elderly patients. Medicine 96(34), 7692 (2017)
Gill, A.Q., Phennel, N., Lane, D., et al.: IoT-enabled emergency information supply chain architecture for elderly people: the Australian context. Inf. Syst. 58, 75–86 (2016)
Varnfield, M., Jayasena, R., O’Dwyer, J., et al.: ISQUA16-2955 home telemonitoring for aged care – do the elderly comply and complete. Int. J. Qual. Health Care 28(suppl_1), 66–67 (2016)
Bicharra Garcia, A.C., Vivacqua, A.S., Sanchez-Pi, N., et al.: Crowd-based ambient assisted living to monitor the elderly’s health outdoors. IEEE Softw. 34(6), 53–57 (2017)
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Yuan, Q. et al. (2022). Design of IoT Health Pension Scheme Based on Physiological and Behavioral Indicators for Elderly. In: Katangur, A., Zhang, LJ. (eds) Services Computing – SCC 2021. SCC 2021. Lecture Notes in Computer Science(), vol 12995. Springer, Cham. https://doi.org/10.1007/978-3-030-96566-2_5
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DOI: https://doi.org/10.1007/978-3-030-96566-2_5
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