RADIO FREQUENCY IDENTIFICATION (RFID)-BASED ELDERLY FALL DETECTION USING LONG SHORT-TERM MEMORY (LSTM) - Clairvoyance Diva P., Damianus
Falls in the elderly can have fatal consequences if not addressed quickly, therefore it’s critical to have a system capable of reliably detecting falls and immediately notifying caregivers. Radio Frequency Identification (RFID) is one such technology. Among various configurations, the most practical setup involves positioning a reader at the edge of the room and attaching multiple passive tags to the elderly’s clothing. The way RFID works is that the reader emits a signal, which is then reflected by the RFID tags and received back by the reader. This signal strength is known as the Received Signal Strength Indicator (RSSI). Obstruction by the elderly’s body, also called body shadowing, can be used to predict indications of a fall event. In similar systems, falls are predicted using classical machine learning algorithms, though trends suggest that models based on Long Short-Term Memory (LSTM) have the potential to deliver better performance. This study shows that pure LSTM achieved 99.39% accuracy and F1macro , while CNN-LSTM achieved perfect accuracy and F1macro