Stress and anxiety detection using facial cues from videos
| Autor: | Dimitris Manousos, Kostas Marias, Matthew Pediaditis, Panagiotis G. Simos, Eleni Kazantzaki, Franco Chiarugi, Giorgos Giannakakis, Manolis Tsiknakis |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
business.industry
Speech recognition Stressor Health Informatics Feature selection 02 engineering and technology Motion (physics) Correlation 03 medical and health sciences 0302 clinical medicine Discriminative model Ranking Signal Processing Stress (linguistics) 0202 electrical engineering electronic engineering information engineering medicine Anxiety 020201 artificial intelligence & image processing Computer vision Artificial intelligence medicine.symptom Psychology business 030217 neurology & neurosurgery |
| Zdroj: | Biomedical Signal Processing and Control. 31:89-101 |
| ISSN: | 1746-8094 |
| DOI: | 10.1016/j.bspc.2016.06.020 |
| Popis: | This study develops a framework for the detection and analysis of stress/anxiety emotional states through video-recorded facial cues. A thorough experimental protocol was established to induce systematic variability in affective states (neutral, relaxed and stressed/anxious) through a variety of external and internal stressors. The analysis was focused mainly on non-voluntary and semi-voluntary facial cues in order to estimate the emotion representation more objectively. Features under investigation included eye-related events, mouth activity, head motion parameters and heart rate estimated through camera-based photoplethysmography. A feature selection procedure was employed to select the most robust features followed by classification schemes discriminating between stress/anxiety and neutral states with reference to a relaxed state in each experimental phase. In addition, a ranking transformation was proposed utilizing self reports in order to investigate the correlation of facial parameters with a participant perceived amount of stress/anxiety. The results indicated that, specific facial cues, derived from eye activity, mouth activity, head movements and camera based heart activity achieve good accuracy and are suitable as discriminative indicators of stress and anxiety. |
| Databáze: | OpenAIRE |
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