6533b85dfe1ef96bd12beaa4
RESEARCH PRODUCT
UBFC-Phys: A Multimodal Database For Psychophysiological Studies of Social Stress
Yannick BenezethPierre De OliveiraJulien ChappéFan YangRita Meziati Saboursubject
Social stressFacial expressionModalitiesComputer scienceSpeech recognition010401 analytical chemistryFeature extraction[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020206 networking & telecommunications02 engineering and technology01 natural sciencesField (computer science)0104 chemical sciencesHuman-Computer InteractionPsychophysiology[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingStress (linguistics)0202 electrical engineering electronic engineering information engineeringTask analysisComputingMilieux_MISCELLANEOUSSoftwaredescription
As humans, we experience social stress in countless everyday-life situations. Giving a speech in front of an audience, passing a job interview, and similar experiences all lead us to go through stress states that impact both our psychological and physiological states. Therefore, studying the link between stress and physiological responses had become a critical societal issue, and recently, research in this field has grown in popularity. However, publicly available datasets have limitations. In this article, we propose a new dataset, UBFC-Phys, collected with and without contact from participants living social stress situations. A wristband was used to measure contact blood volume pulse (BVP) and electrodermal activity (EDA) signals. Video recordings allowed to compute remote pulse signals, using remote photoplethysmography (RPPG), and facial expression features. Pulse rate variability (PRV) was extracted from BVP and RPPG signals. Our dataset permits to evaluate the possibility of using video-based physiological measures compared to more conventional contact-based modalities. The goal of this article is to present both the dataset, which we make publicly available, and experimental results of contact and non-contact data comparison, as well as stress recognition. We obtained a stress state recognition accuracy of 85.48%, achieved by remote PRV features.
year | journal | country | edition | language |
---|---|---|---|---|
2021-02-01 | IEEE Transactions on Affective Computing |