SPATIO-TEMPORAL CONVOLUTIONAL NEURAL NETWORK FOR ELDERLY FALL DETECTION IN DEPTH VIDEO CAMERAS

Emergency departments treat around 2.5 million older people for fall injuries each year. Preserving the elderlys' right of aging in a home of their own choice is mandatory in today's world, as more elderly people are willing to live independently. Current implementations of fall detection systems lack accuracy. Despite efforts to detect elderly falls, it is possible that daily life activities, such as lying down, trigger false alarms. Moreover, privacy is the main concern for visual cameras. In this research we used deep convolutional neural networks to describe the overall space-time appearance pattern of a fall-event in depth video cameras. We developed a 3D convolutional neural network to capture both the spatial information available in video frames, and the temporal information presented through successive video frames. Our method outperformed the state-of-the art accuracy with a large margin.