Abstract
The chances of surviving a fall, heart attack, and stroke are six times greater if the elderly get emergency assistance within an hour of incidence. Therefore, the elderly real-time healthcare monitoring system is necessary to reduce the anxiety of them and the risk of accidents. The purpose of this study is to successfully detect and generate alarms in cases of sudden stroke onset while doing physical activity and exercise. The purpose would be done by the development of an elderly health monitoring system, which is controlled by hyper-connected self-machine learning engine. The components of the system are a knowledge base, real-time data monitoring, network security, and self-learning engine. The knowledge base would have risk factors, medical health records, psychological factors, gait and motion patterns, and bio-signals. The old peoples’ activities are monitored in real-time through wearable sensors. The method mentioned above and its frameworks will be discussed in this paper.
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Acknowledgments
This work was supported by the National Research Council of Science & Technology (NST) grant by the Korea government (MSIP) (No. CRC-15-05-ETRI).
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Park, S.J., Subramaniyam, M., Hong, S., Kim, D. (2018). Service Based Healthcare Monitoring System for the Elderly - Physical Activity and Exercise. In: Duffy, V., Lightner, N. (eds) Advances in Human Factors and Ergonomics in Healthcare and Medical Devices. AHFE 2017. Advances in Intelligent Systems and Computing, vol 590. Springer, Cham. https://doi.org/10.1007/978-3-319-60483-1_34
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DOI: https://doi.org/10.1007/978-3-319-60483-1_34
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