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.
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 |
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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) |
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ISSN (Print) | 2156-8103 |