Indoor fall detection using a network of seismic sensors

Falls present a great health threat as people get older; moreover, studies have shown that rapid response is critical to decreasing fall-related mortality. Thus, the development of signal processing algorithms for sensors used in biomedical applications involving assisted living has become an avid area of research. In this work, a novel algorithm for activity classification and fall detection using a seismic sensor network is proposed. More specifically, classification of falling as well as sources of parasitic signals, such as dropping an object, slamming a door, and shutting a window, are considered. A new target detection and feature extraction algorithm based on wavelet coefficient characterization and spectral statistics is proposed. Results quantifying the performance of the algorithm on real data from a seismic sensor network are given. It is shown that the algorithm offers a reduction of false alarms especially in the case of potentially confusable parasitic signals. According to the studies human falls were detected with an accuracy rate of 99.4%.