Contactless Fall Detection using RFID Wall and AI

Fall detection (FD) in elderly people is crucial for preventing serious injuries that could lead to prolonged dependence and even death in severe cases. The world health organization reports that 50% of elderly people fall annually, underscoring the need for early FD to prevent hospitalized or dying in accidents. Contactless FD systems have developed as a viable alternative to wearable sensor-based systems for detecting falls amid concern to security and privacy. This paper proposes a contactless FD system that leverages a passive UHF RFID tag array to measure the received signal strength indicator (RSSI) and utilizes deep learning (DL) to accurately predict fall activity by observing RSSI fluctuations. The system can effectively differentiate between standing and falling activities by training the DL-based classifiers on features extracted from raw data. Our proposed contactless system is capable of detecting indoor falling activity with an accuracy of 95%, which demonstrates the efficacy of the approach.