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

    17 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
    @inproceedings{d0cdac64138548e4b7f06a1fb056ea37,
    title = "Relative Body Parts Movement for Automatic Depression Analysis",
    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.",
    keywords = "face analysis, human body parts movement, Depression analysis",
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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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    AU - Dhall, Abhinav

    AU - Goecke, Roland

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    AB - 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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    DO - 10.1109/ACII.2013.87

    M3 - Conference contribution

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    BT - Fifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction

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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