Relative Body Parts Movement for Automatic Depression Analysis

Jyoti Joshi, Abhinav Dhall, Roland Goecke, Jeffrey Cohn

Research output: A Conference proceeding or a Chapter in BookConference contribution

20 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 publicationFifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction
EditorsThierry Pun, Catherine Pelachaud, Nicu Sebe
Place of PublicationGeneva, Switzerland
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages492-497
Number of pages6
ISBN (Electronic)9780769550480
ISBN (Print)9781479906321
DOIs
Publication statusPublished - 2013
EventFifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction: ACII 2013 - Emotion, Technology, Humanities - Geneva, Geneva, Switzerland
Duration: 2 Sep 20135 Sep 2013
http://Fifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction (Conference Link)

Conference

ConferenceFifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction
Abbreviated titleACII 2013
CountrySwitzerland
CityGeneva
Period2/09/135/09/13
Internet address

Fingerprint

Support vector machines
Classifiers
Monitoring
Experiments
Motion analysis

Cite this

Joshi, J., Dhall, A., Goecke, R., & Cohn, J. (2013). Relative Body Parts Movement for Automatic Depression Analysis. In T. Pun, C. Pelachaud, & N. Sebe (Eds.), Fifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction (pp. 492-497). Geneva, Switzerland: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ACII.2013.87
Joshi, Jyoti ; Dhall, Abhinav ; Goecke, Roland ; Cohn, Jeffrey. / Relative Body Parts Movement for Automatic Depression Analysis. Fifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction. editor / Thierry Pun ; Catherine Pelachaud ; Nicu Sebe. Geneva, Switzerland : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 492-497
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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.",
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Joshi, J, Dhall, A, Goecke, R & Cohn, J 2013, Relative Body Parts Movement for Automatic Depression Analysis. in T Pun, C Pelachaud & N Sebe (eds), Fifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction. IEEE, Institute of Electrical and Electronics Engineers, Geneva, Switzerland, pp. 492-497, Fifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction, Geneva, Switzerland, 2/09/13. https://doi.org/10.1109/ACII.2013.87

Relative Body Parts Movement for Automatic Depression Analysis. / Joshi, Jyoti; Dhall, Abhinav; Goecke, Roland; Cohn, Jeffrey.

Fifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction. ed. / Thierry Pun; Catherine Pelachaud; Nicu Sebe. Geneva, Switzerland : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 492-497.

Research output: A Conference proceeding or a Chapter in BookConference contribution

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Joshi J, Dhall A, Goecke R, Cohn J. Relative Body Parts Movement for Automatic Depression Analysis. In Pun T, Pelachaud C, Sebe N, editors, Fifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction. Geneva, Switzerland: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 492-497 https://doi.org/10.1109/ACII.2013.87