Vision-Based Elderly Fall Detection Algorithm for Mobile Robot

Fall ranks first among the elderly aged 65 and above. In order to detect falls of the elderly living alone, we use a vision-based detection method to complete fall detection for the elderly on a low-cost small mobile robot. We propose a deep learning fall detection framework for mobile robot. In this framework, Raspberry Pi 4 Model B is selected as hardware platform for mobile robot, and lightweight NanoDet-Lite is used for fall detection. The mAP of our method is 0.912 and model size is only 2.17MB. Our method works more than 3 times faster than YOLOv3-tiny on Raspberry Pi without any hardware accelerator. In ncnn framework, NanoDet-Lite works at 22.03 FPS on Raspberry Pi and mAP reaches 0.902. The results show that our method not only can be applied to the low-cost mobile robot, but also has a good detection performance.