Embedded Real-Time Fall Detection Using Deep Learning For Elderly Care
This addresses safety monitoring for the elderly with a privacy-preserving, embedded solution, though it is incremental as it adapts deep learning to a new sensor type.
The paper tackles real-time fall detection for elderly care by proposing a system using a Dynamic Vision Sensor (DVS) and a DVS-Temporal Network, achieving 95.5% F1-score and 31.25 FPS on embedded hardware.
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.