Relative body parts movement for automatic depression analysis

Jyoti Joshi, Abhinav Dhall, Roland Goecke, Jeffrey F. Cohn

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

30 Citations (Scopus)

Abstract

In this paper, a human body part motion analysis based approach is proposed for depression analysis. Depression is a serious psychological disorder. The absence of an (automated) objective diagnostic aid for depression leads to a range of subjective biases in initial diagnosis and ongoing monitoring. Researchers in the affective computing community have approached the depression detection problem using facial dynamics and vocal prosody. Recent works in affective computing have shown the significance of body pose and motion in analysing the psychological state of a person. Inspired by these works, we explore a body parts motion based approach. Relative orientation and radius are computed for the body parts detected using the pictorial structures framework. A histogram of relative parts motion is computed. To analyse the motion on a holistic level, space-time interest points are computed and a bag of words framework is learnt. The two histograms are fused and a support vector machine classifier is trained. The experiments conducted on a clinical database, prove the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013
Pages492-497
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2013
Externally publishedYes
Event2013 5th Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013 - Geneva, Switzerland
Duration: 2 Sep 20135 Sep 2013

Publication series

NameProceedings - 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013

Conference

Conference2013 5th Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013
Country/TerritorySwitzerland
CityGeneva
Period2/09/135/09/13

Keywords

  • Automatic depression detection
  • Body movement analysis

Cite this