Heart rate estimation from facial videos for depression analysis

Aamir Mustafa; Shalini Bhatia; Munawar Hayat; Roland Goecke

doi:10.1109/ACII.2017.8273645

Heart rate estimation from facial videos for depression analysis

Aamir Mustafa, Shalini Bhatia, Munawar Hayat, Roland Goecke

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

6 Citations (Scopus)

Abstract

Automated facial video analysis is useful in numerous health care applications. For example, spatio-temporal analysis of such videos has been previously 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 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 language	English
Title of host publication	The 7th International Conference on Affective Computing and Intelligent Interaction (ACII 2017)
Editors	Hayley Hung, Emily Mower Provost, Mohammad Soleymani
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	498-503
Number of pages	6
ISBN (Electronic)	9781538605639
ISBN (Print)	9781538605646
DOIs	https://doi.org/10.1109/ACII.2017.8273645
Publication status	Published - 2017
Externally published	Yes
Event	International Conference on Affective Computing and Intelligent Interaction 2017 - San Antonio, United States of America Duration: 23 Oct 2017 → 26 Oct 2017 Conference number: 7th http://acii2017.org/Home

Conference

Conference	International Conference on Affective Computing and Intelligent Interaction 2017
Abbreviated title	ACII 2017
Country/Territory	United States of America
City	San Antonio
Period	23/10/17 → 26/10/17
Internet address	http://acii2017.org/Home

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ACII.2017.8273645

Cite this

Mustafa, A., Bhatia, S., Hayat, M., & Goecke, R. (2017). Heart rate estimation from facial videos for depression analysis. In H. Hung, E. Mower Provost, & M. Soleymani (Eds.), The 7th International Conference on Affective Computing and Intelligent Interaction (ACII 2017) (pp. 498-503). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ACII.2017.8273645

Mustafa, Aamir ; Bhatia, Shalini ; Hayat, Munawar et al. / Heart rate estimation from facial videos for depression analysis. The 7th International Conference on Affective Computing and Intelligent Interaction (ACII 2017). editor / Hayley Hung ; Emily Mower Provost ; Mohammad Soleymani. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 498-503

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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 been previously 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 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.",

author = "Aamir Mustafa and Shalini Bhatia and Munawar Hayat and Roland Goecke",

year = "2017",

doi = "10.1109/ACII.2017.8273645",

language = "English",

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pages = "498--503",

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booktitle = "The 7th International Conference on Affective Computing and Intelligent Interaction (ACII 2017)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "International Conference on Affective Computing and Intelligent Interaction 2017, ACII 2017 ; Conference date: 23-10-2017 Through 26-10-2017",

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Mustafa, A, Bhatia, S, Hayat, M & Goecke, R 2017, Heart rate estimation from facial videos for depression analysis. in H Hung, E Mower Provost & M Soleymani (eds), The 7th International Conference on Affective Computing and Intelligent Interaction (ACII 2017). IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 498-503, International Conference on Affective Computing and Intelligent Interaction 2017, San Antonio, Texas, United States of America, 23/10/17. https://doi.org/10.1109/ACII.2017.8273645

Heart rate estimation from facial videos for depression analysis. / Mustafa, Aamir; Bhatia, Shalini; Hayat, Munawar et al.
The 7th International Conference on Affective Computing and Intelligent Interaction (ACII 2017). ed. / Hayley Hung; Emily Mower Provost; Mohammad Soleymani. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 498-503.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

TY - GEN

T1 - Heart rate estimation from facial videos for depression analysis

AU - Mustafa, Aamir

AU - Bhatia, Shalini

AU - Hayat, Munawar

AU - Goecke, Roland

N1 - Conference code: 7th

PY - 2017

Y1 - 2017

N2 - Automated facial video analysis is useful in numerous health care applications. For example, spatio-temporal analysis of such videos has been previously 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 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.

AB - Automated facial video analysis is useful in numerous health care applications. For example, spatio-temporal analysis of such videos has been previously 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 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.

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BT - The 7th International Conference on Affective Computing and Intelligent Interaction (ACII 2017)

A2 - Hung, Hayley

A2 - Mower Provost, Emily

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PB - IEEE, Institute of Electrical and Electronics Engineers

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T2 - International Conference on Affective Computing and Intelligent Interaction 2017

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ER -

Mustafa A, Bhatia S, Hayat M, Goecke R. Heart rate estimation from facial videos for depression analysis. In Hung H, Mower Provost E, Soleymani M, editors, The 7th International Conference on Affective Computing and Intelligent Interaction (ACII 2017). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 498-503 doi: 10.1109/ACII.2017.8273645

Heart rate estimation from facial videos for depression analysis

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