Detecting Depression Through Gait Data: Examining the Contribution of Gait Features in Recognizing Depression.

ABSTRACT: While depression is one of the most common mental disorders affecting more than 300 million people across the world, it is often left undiagnosed. This paper investigated the association between depression and gait characteristics with the aim to assist in diagnosing depression. Our dataset consisted of 121 healthy people and 126 patients with depression who diagnosed by psychiatrists according to the Diagnostic and Statistical Manual of Mental Disorders. Spatiotemporal, temporal-domain, and frequency-domain features were extracted based on the walking data of 247 participants recorded by Microsoft Kinect (Version 2). Multiple logistic regression was used to analyze the variance of spatiotemporal (12.55%), time-domain (58.36%), and frequency-domain features (60.71%) on recognizing depressi

SUBMITTER: Wang Y 

PROVIDER: S-EPMC8138135 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature