Internet of Things for fall prediction and prevention

Abstract

Internet of Things (IoT) is making a breakthrough for the development of innovative healthcare systems. IoT-based health applications are expected to change the paradigm traditionally followed by physicians for diagnosis, by moving health monitoring from the clinical environment to the domestic space. Fall avoidance is a field where the continuous monitoring allowed by the IoT-based framework offers tremendous benefits to the user. In fact, falls are highly damaging due to both physical and psychological injuries. Currently, the most promising approaches to reduce fall injuries are fall prediction, which strives to predict a fall before its occurrence, and fall prevention, which assesses balance and muscle strength through some clinical functional tests. In this context, the IoT-based framework provides real-time emergency notification as soon as fall is predicted, mid-term analysis on the monitored activities, and data logging for long-term analysis by clinical experts. This approach gives more information to experts for estimating the risk of a future fall and for suggesting proper exercises.