Falling detection based on deep learning and video classifier
Xu Chen, Jinbo Liu, Yihang Liu, Chonghan Wang · 2022
Nowadays, falls are a common problem for older adults, which leads to injuries and decreased quality of life. According to the Chinese Center for Disease Control and Prevention, falls have become the leading cause of injuries to people over 65. Therefore, how to detect falls quickly becomes very important. In this paper, we assess the performance of five different video classification algorithms, namely, Efficient Convolutional Network for Online Video Understanding (ECO), C3D network, Temporal Segment Network (TSN), NeXtVLAD, Convolutional two-stream Network. Finally, we find that ECO and TSN algorithms perform better, and their accuracy is around 95%. However, ECO has a shorter running time and a faster response, so the ECO algorithm performs best in detecting falls.