Embedded Real-Time Fall Detection Using Deep Learning For Elderly Care

• DOI: 10.48550/arxiv.1711.11200
• Publication Date: 2017-01-01
• Authors: Lee, Hyunwoo; Kim, Jooyoung; Yang, Dojun; Kim, Joon-Ho
• Keywords: FOS: Computer and information sciences; 03 medical and health sciences; 0302 clinical medicine; Artificial Intelligence (cs.AI); Statistics - Machine Learning; Computer Science - Artificial Intelligence; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; Machine Learning (stat.ML)

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.<br/>5 pages<br/>