Title: Embedded Real-Time Fall Detection Using Deep Learning For Elderly Care,
Abstract: This paper proposes a real-time embedded fall detection system using a
DVS(Dynamic Vision Sensor) that has never been used for traditional fall
detection, a dataset for fall detection using that, and a DVS-TN(DVS-Temporal
Network). The first contribution is building a DVS Falls Dataset, which made
our network to recognize a much greater variety of falls than the existing
datasets that existed before and solved privacy issues using the DVS. Secondly,
we introduce the DVS-TN : optimized deep learning network to detect falls using
DVS. Finally, we implemented a fall detection system which can run on
low-computing H/W with real-time, and tested on DVS Falls Dataset that takes
into account various falls situations. Our approach achieved 95.5% on the
F1-score and operates at 31.25 FPS on NVIDIA Jetson TX1 board.