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.
Cite
@article{arxiv.1711.11200,
title = {Embedded Real-Time Fall Detection Using Deep Learning For Elderly Care},
author = {Hyunwoo Lee and Jooyoung Kim and Dojun Yang and Joon-Ho Kim},
journal= {arXiv preprint arXiv:1711.11200},
year = {2017}
}