Embedded Fall Detection with a Neural Network and Bio-Inspired Stereo Vision

Abstract

In this paper, we present a bio-inspired, purely passive, and embedded fall detection system for its application towards safety for elderly at home. Bio-inspired means the use of two optical detector chips with event-driven pixels that are sensitive to relative light intensity changes only. The two chips are used as stereo configuration which enables a 3D representation of the observed area with a stereo matching technique. In contrast to conventional digital cameras, this image sensor delivers asynchronous events instead of synchronous intensity or color images, thus, the privacy issue is systematically solved. Another advantage is that stationary installed fall detection systems have a better acceptance for independent living compared to permanently worn devices. The fall detection is done by a trained neural network. First, a meaningful feature vector is calculated from the point clouds, then the neural network classifies the actual event as fall or non-fall. All processing is done on an embedded device consisting of an FPGA for stereo matching and a DSP for neural network calculation achieving several fall evaluations per second. The results evaluation showed that our fall detection system achieves a fall detection rate of more than 96% with false positives below 5% for our prerecorded dataset consisting of 679 fall scenarios.