Title

Falling Detection Based on Deep Learning and Video Classifier

Authors

Chen Xu, University of Nottingham Ningbo China
Jinbo Liu, Troy University
Yihang Liu, Wenzhou-Kean University
Chonghan Wang, University of California, Davis

Document Type

Conference Proceeding

Publication Date

1-1-2022

Abstract

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.

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

DOI

10.1117/12.2641030

Recommended Citation

Xu, Chen; Liu, Jinbo; Liu, Yihang; and Wang, Chonghan, "Falling Detection Based on Deep Learning and Video Classifier" (2022). Kean Publications. 723.
https://digitalcommons.kean.edu/keanpublications/723