Published Paper Details:

Falls have serious consequences and are prevalent in acute hospitals and nursing homes caring for older people Fall lead to many adverse consequences for the patients apart from physical injuries such as anxiety, depression and loss of independence. Technological interventions to reduce the risk of falling aim to monitor automatically monitor bed-exit events and subsequently alert healthcare personnel to provide timely supervisions. In that sense, the present work proposes an innovative IoT-based system for detecting falls of elderly people in indoor environments, which takes advantages of low-power wireless sensor networks and smart devices. For this purpose, we recognize the potential to employ a low resolution acceleration sensing modality in contrast to powering and sensing with a conventional MEMS (Micro Electro Mechanical System) accelerometer. A 3D-axis passive accelerometer with gyroscope embedded into a wearable sensor device is used, which is responsible for collecting data from movements of elderly people in real-time. The accelerometer data from the devices are continuously sent to a multithreaded server which hosts a pre- trained machine learning model that analyzes the data to determine whether a fall has occurred or not. Finally this system provides service built on cloud. If a fall is detected, an alert is activated and the system reacts automatically by sending notifications to the groups responsible for the care of the elderly people.