Can Body Expressions Contribute to Automatic Depression Analysis?

Jyoti Joshi, Roland GOECKE, Gordon Parker, Michael Breakspear

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

34 Citations (Scopus)
5 Downloads (Pure)

Abstract

Depression is one of the most common mental health disorders with strong adverse effects on personal and social functioning. The absence of any objective diagnostic aid for depression leads to a range of subjective biases in initial diagnosis and ongoing monitoring. Psychologists use various visual cues in their assessment to quantify depression such as facial expressions, eye contact and head movements. This paper studies the contribution of (upper) body expressions and gestures for automatic depression analysis. A framework based on space-time interest points and bag of words is proposed for the analysis of upper body and facial movements. Salient interest points are selected using clustering. The major contribution of this paper lies in the creation of a bag of body expressions and a bag of facial dynamics for assessing the contribution of different body parts for depression analysis. Head movement analysis is performed by selecting rigid facial fiducial points and a new histogram of head movements is proposed. The experiments are performed on real-world clinical data where video clips of patients and healthy controls are recorded during interactive interview sessions. The results show the effectiveness of the proposed system to evaluate the contribution of various body parts in depression analysis.
Original languageEnglish
Title of host publicationIEEE International Conference on Automated Face and Gesture Recognition FG2013
EditorsRama Chellappa, Xilin Chen, Qiang Ji
Place of PublicationShanghai, China
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-7
Number of pages7
Volume1
ISBN (Print)9781467355452
DOIs
Publication statusPublished - 2013
EventIEEE International Conference on Automated Face and Gesture Recognition FG2013 - Shanghai, Shanghai, China
Duration: 22 Apr 201326 Apr 2013

Conference

ConferenceIEEE International Conference on Automated Face and Gesture Recognition FG2013
CountryChina
CityShanghai
Period22/04/1326/04/13

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Cite this

Joshi, J., GOECKE, R., Parker, G., & Breakspear, M. (2013). Can Body Expressions Contribute to Automatic Depression Analysis? In R. Chellappa, X. Chen, & Q. Ji (Eds.), IEEE International Conference on Automated Face and Gesture Recognition FG2013 (Vol. 1, pp. 1-7). Shanghai, China: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/FG.2013.6553796
Joshi, Jyoti ; GOECKE, Roland ; Parker, Gordon ; Breakspear, Michael. / Can Body Expressions Contribute to Automatic Depression Analysis?. IEEE International Conference on Automated Face and Gesture Recognition FG2013. editor / Rama Chellappa ; Xilin Chen ; Qiang Ji. Vol. 1 Shanghai, China : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 1-7
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title = "Can Body Expressions Contribute to Automatic Depression Analysis?",
abstract = "Depression is one of the most common mental health disorders with strong adverse effects on personal and social functioning. The absence of any objective diagnostic aid for depression leads to a range of subjective biases in initial diagnosis and ongoing monitoring. Psychologists use various visual cues in their assessment to quantify depression such as facial expressions, eye contact and head movements. This paper studies the contribution of (upper) body expressions and gestures for automatic depression analysis. A framework based on space-time interest points and bag of words is proposed for the analysis of upper body and facial movements. Salient interest points are selected using clustering. The major contribution of this paper lies in the creation of a bag of body expressions and a bag of facial dynamics for assessing the contribution of different body parts for depression analysis. Head movement analysis is performed by selecting rigid facial fiducial points and a new histogram of head movements is proposed. The experiments are performed on real-world clinical data where video clips of patients and healthy controls are recorded during interactive interview sessions. The results show the effectiveness of the proposed system to evaluate the contribution of various body parts in depression analysis.",
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Joshi, J, GOECKE, R, Parker, G & Breakspear, M 2013, Can Body Expressions Contribute to Automatic Depression Analysis? in R Chellappa, X Chen & Q Ji (eds), IEEE International Conference on Automated Face and Gesture Recognition FG2013. vol. 1, IEEE, Institute of Electrical and Electronics Engineers, Shanghai, China, pp. 1-7, IEEE International Conference on Automated Face and Gesture Recognition FG2013, Shanghai, China, 22/04/13. https://doi.org/10.1109/FG.2013.6553796

Can Body Expressions Contribute to Automatic Depression Analysis? / Joshi, Jyoti; GOECKE, Roland; Parker, Gordon; Breakspear, Michael.

IEEE International Conference on Automated Face and Gesture Recognition FG2013. ed. / Rama Chellappa; Xilin Chen; Qiang Ji. Vol. 1 Shanghai, China : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 1-7.

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

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N2 - Depression is one of the most common mental health disorders with strong adverse effects on personal and social functioning. The absence of any objective diagnostic aid for depression leads to a range of subjective biases in initial diagnosis and ongoing monitoring. Psychologists use various visual cues in their assessment to quantify depression such as facial expressions, eye contact and head movements. This paper studies the contribution of (upper) body expressions and gestures for automatic depression analysis. A framework based on space-time interest points and bag of words is proposed for the analysis of upper body and facial movements. Salient interest points are selected using clustering. The major contribution of this paper lies in the creation of a bag of body expressions and a bag of facial dynamics for assessing the contribution of different body parts for depression analysis. Head movement analysis is performed by selecting rigid facial fiducial points and a new histogram of head movements is proposed. The experiments are performed on real-world clinical data where video clips of patients and healthy controls are recorded during interactive interview sessions. The results show the effectiveness of the proposed system to evaluate the contribution of various body parts in depression analysis.

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Joshi J, GOECKE R, Parker G, Breakspear M. Can Body Expressions Contribute to Automatic Depression Analysis? In Chellappa R, Chen X, Ji Q, editors, IEEE International Conference on Automated Face and Gesture Recognition FG2013. Vol. 1. Shanghai, China: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 1-7 https://doi.org/10.1109/FG.2013.6553796