Heart Rate Estimation From Facial Videos for Depression Analysis

Aamir Mustafa, Shalini Bhatia, Munawar Hayat, Roland Goecke

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

    Abstract

    Automated facial video analysis is useful in numerous health care applications. For example, spatio-temporal analysis of such videos has previously been done for assisting clinicians in the diagnosis of depression. Physiological measures, such as an individual’s heart rate, provide very important cues to understand a person’s mental health. Unobtrusively estimated heart rate has not been previously used to analyse an individuals’ mental health. In this paper, we automatically estimate heart rate activity from facial videos. We then study the association of the estimated heart rate activity with the person’s mental health, as diagnosed by clinicians. Specifically, from the heart rate activity in response
    to watching different movies, we classify individuals as either depressed or healthy. The efficacy of the proposed scheme is demonstrated by experimental evaluations on a clinically validated dataset. Our results suggest unobtrusively estimated heart rate to be very effective for depression analysis.
    Original languageEnglish
    Title of host publicationProceedings of the 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII 2017)
    Place of PublicationSan Antonio, TX, USA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages498-503
    Number of pages6
    ISBN (Electronic)9781538605639
    ISBN (Print)9781538605646
    DOIs
    Publication statusPublished - 23 Oct 2017

    Publication series

    Name2017 SEVENTH INTERNATIONAL CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII)
    ISSN (Print)2156-8103

    Cite this

    Mustafa, A., Bhatia, S., Hayat, M., & Goecke, R. (2017). Heart Rate Estimation From Facial Videos for Depression Analysis. In Proceedings of the 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII 2017) (pp. 498-503). (2017 SEVENTH INTERNATIONAL CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII)). San Antonio, TX, USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ACII.2017.8273645
    Mustafa, Aamir ; Bhatia, Shalini ; Hayat, Munawar ; Goecke, Roland. / Heart Rate Estimation From Facial Videos for Depression Analysis. Proceedings of the 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII 2017). San Antonio, TX, USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 498-503 (2017 SEVENTH INTERNATIONAL CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII)).
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    title = "Heart Rate Estimation From Facial Videos for Depression Analysis",
    abstract = "Automated facial video analysis is useful in numerous health care applications. For example, spatio-temporal analysis of such videos has previously been done for assisting clinicians in the diagnosis of depression. Physiological measures, such as an individual’s heart rate, provide very important cues to understand a person’s mental health. Unobtrusively estimated heart rate has not been previously used to analyse an individuals’ mental health. In this paper, we automatically estimate heart rate activity from facial videos. We then study the association of the estimated heart rate activity with the person’s mental health, as diagnosed by clinicians. Specifically, from the heart rate activity in responseto watching different movies, we classify individuals as either depressed or healthy. The efficacy of the proposed scheme is demonstrated by experimental evaluations on a clinically validated dataset. Our results suggest unobtrusively estimated heart rate to be very effective for depression analysis.",
    keywords = "Heart rate, Depression analysis, Video analysis, Physioloical measures",
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    Mustafa, A, Bhatia, S, Hayat, M & Goecke, R 2017, Heart Rate Estimation From Facial Videos for Depression Analysis. in Proceedings of the 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII 2017). 2017 SEVENTH INTERNATIONAL CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII), IEEE, Institute of Electrical and Electronics Engineers, San Antonio, TX, USA, pp. 498-503. https://doi.org/10.1109/ACII.2017.8273645

    Heart Rate Estimation From Facial Videos for Depression Analysis. / Mustafa, Aamir; Bhatia, Shalini; Hayat, Munawar; Goecke, Roland.

    Proceedings of the 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII 2017). San Antonio, TX, USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 498-503 (2017 SEVENTH INTERNATIONAL CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII)).

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

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    T1 - Heart Rate Estimation From Facial Videos for Depression Analysis

    AU - Mustafa, Aamir

    AU - Bhatia, Shalini

    AU - Hayat, Munawar

    AU - Goecke, Roland

    PY - 2017/10/23

    Y1 - 2017/10/23

    N2 - Automated facial video analysis is useful in numerous health care applications. For example, spatio-temporal analysis of such videos has previously been done for assisting clinicians in the diagnosis of depression. Physiological measures, such as an individual’s heart rate, provide very important cues to understand a person’s mental health. Unobtrusively estimated heart rate has not been previously used to analyse an individuals’ mental health. In this paper, we automatically estimate heart rate activity from facial videos. We then study the association of the estimated heart rate activity with the person’s mental health, as diagnosed by clinicians. Specifically, from the heart rate activity in responseto watching different movies, we classify individuals as either depressed or healthy. The efficacy of the proposed scheme is demonstrated by experimental evaluations on a clinically validated dataset. Our results suggest unobtrusively estimated heart rate to be very effective for depression analysis.

    AB - Automated facial video analysis is useful in numerous health care applications. For example, spatio-temporal analysis of such videos has previously been done for assisting clinicians in the diagnosis of depression. Physiological measures, such as an individual’s heart rate, provide very important cues to understand a person’s mental health. Unobtrusively estimated heart rate has not been previously used to analyse an individuals’ mental health. In this paper, we automatically estimate heart rate activity from facial videos. We then study the association of the estimated heart rate activity with the person’s mental health, as diagnosed by clinicians. Specifically, from the heart rate activity in responseto watching different movies, we classify individuals as either depressed or healthy. The efficacy of the proposed scheme is demonstrated by experimental evaluations on a clinically validated dataset. Our results suggest unobtrusively estimated heart rate to be very effective for depression analysis.

    KW - Heart rate

    KW - Depression analysis

    KW - Video analysis

    KW - Physioloical measures

    UR - https://ieeexplore.ieee.org/document/8273645/

    U2 - 10.1109/ACII.2017.8273645

    DO - 10.1109/ACII.2017.8273645

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    T3 - 2017 SEVENTH INTERNATIONAL CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII)

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    Mustafa A, Bhatia S, Hayat M, Goecke R. Heart Rate Estimation From Facial Videos for Depression Analysis. In Proceedings of the 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII 2017). San Antonio, TX, USA: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 498-503. (2017 SEVENTH INTERNATIONAL CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII)). https://doi.org/10.1109/ACII.2017.8273645