Real-Time Privacy-Preserving Fall Detection Using Dynamic Vision Sensors

In this fast-paced world, the only people who have to stay at home for the longest time are the sick or the elderly. Indoor surveillance for monitoring the safety of these individuals in real-time could help in the detection and prevention of fatal accidents. Achieving this task while preserving privacy brings us to the new research trend of privacy-preserving human fall detection. In this paper, we attempt to solve the problem of privacy-preserving fall detection, a subtask of human action recognition, using the Dynamic Vision Sensor (DVS). We demonstrate the effectiveness of this approach by performing real-time fall detection with a 3D-Convolutional Neural Network (3D-CNN). Our proposed methods achieved average sensitivity and specificity of 99.34% and 100% respectively. Due to the small memory footprint, number parameters, and operation count of the model, we could achieve real-time performance on an edge device.