Abstract
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 | Proceedings of the 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII 2017) |
| Place of Publication | San Antonio, TX, USA |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 498-503 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538605639 |
| ISBN (Print) | 9781538605646 |
| DOIs | |
| Publication status | Published - 23 Oct 2017 |
Publication series
| Name | 2017 SEVENTH INTERNATIONAL CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII) |
|---|---|
| ISSN (Print) | 2156-8103 |
Cite this
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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 Book › Conference contribution
TY - GEN
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
M3 - Conference contribution
SN - 9781538605646
T3 - 2017 SEVENTH INTERNATIONAL CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII)
SP - 498
EP - 503
BT - Proceedings of the 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII 2017)
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - San Antonio, TX, USA
ER -