Depression risk recognition based on gait: A benchmark

Abstract

Recently, depression recognition has received considerable attention. Due to easy acquisition at a distance, gait-based depression recognition can be a useful tool for auxiliary diagnosis and self-help depression risk assessment. Most existing methods use a few hand-craft features for analysis. To further investigate the relationship between depression and gait, we collect a gait-based depression dataset named D-Gait. 27,120 gait sequences of 292 volunteers with informed consent are collected to support the data-driven methodology (the private information is eliminated). Multiple shooting angles and three kinds of clothing are taken into consideration. To the best of our knowledge, it is the first published gait-based depression dataset. Based on this dataset, a gait-based depression risk recognition benchmark is established. We systematically investigate representative methods with skeleton and silhouette data, thereby verifying most psychological research conclusions about the relationship between gait and depression. Besides, more instructive insights are given in our paper, which indicates the significant potential of gait-based depression risk recognition. The benchmark will advance the research on depression, and it will be released with this paper.