Fall Detection Based on Fusion of Passive and Active Acoustic Sensing

Diannan Chen, Aslan B. Wong, Kaishun Wu

Abstract: Numerous studies have extensively examined the living conditions of older adults who live alone, highlighting falls as the most significant threat to their well-being. Recognizing the critical importance of fall detection for older adults, researchers have conducted numerous investigations over the past decade to develop fall detection systems utilizing various detection methods. In this article, we further explore the potential of acoustic signals in detecting fall events and propose a novel system called FA-Fall, which fully harnesses acoustic signals for fall detection. Our approach revolves around fusing passive and active acoustic sensing through a pair of audio transceivers. We design a multimodal classification framework that integrates an attention mechanism and an anomaly detection mechanism to capitalize on the complementary and redundancy of passive and active sensing. We implemented the FA-Fall system and carried out extensive experiments to evaluate its performance. The results demonstrate that FA-Fall achieves an impressive overall accuracy of 98.97% under typical environmental conditions. Furthermore, it can still detect fall events with over 90% accuracy in challenging environments characterized by considerable background noise or nonline-of-sight conditions.