FallCare+: An IoT surveillance system for fall detection
Charles C.-H. Hsu, Michael Y.-C. Wang, Hsien C.H. Shen, Ranma H.-C. Chiang, Charles H.‐P. Wen · 2017
Recently, the rapid prevalence of mobile devices leads to diversified applications of intelligent IoT systems (including connected devices and cloud computing) for services. Better performance and efficiency can be achieved from well planning and utilizing computing resources. In this paper, we try to explain the system development of communication and computing for fall detection. Feature extraction is the first task along with live video streaming, and the machine learning techniques are involved to determine the fall event. During integrating various IoT components, our system has four developing stages, and is incrementally improved for better performance. We aim at providing experience as a reference for other homecare or medical services in the future.