Toward Automatic Detection of Acute Stress: Relevant Nonverbal Behaviors and Impact of Personality Traits

David Antonio Gómez Jáuregui (1) , Carole Castanier (2) , Bingbing Chang (2) , Michaël Val (2) , François Cottin (2) , Christine Le Scanff (2) , Jean-Claude Martin (3)

1 ESTIA INSTITUTE OF TECHNOLOGY
2 CIAMS - Complexité, Innovation, Activités Motrices et Sportives
3 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur

The aim of the present study is to identify relevant nonverbal features allowing the discrimination of different stressful behaviors, with the consideration of personality factors. In order to achieve this aim, we propose a new method for psychological stress induction involving four different stressful tasks. The proposed protocol was tested with 45 PhD students and the analysis of heart rate variability suggests that stress was indeed elicited. PhD students were selected as participants because they often experience stress. Multimodal data was collected and analyzed in order to identify nonverbal behavioral features related to the different stressful tasks. The psychological profile of participants was taken into account to understand how different stressful behaviors are correlated with personality factors. Results suggest that relevant nonverbal behaviors can discriminate between stressful tasks. In addition, relevant behaviors involving movement variability appear to be correlated with personality factors and stressful tasks.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement des images [eess.IV]

David Antonio Gómez Jáuregui, Carole Castanier, Bingbing Chang, Michaël Val, François Cottin, et al.. Toward Automatic Detection of Acute Stress: Relevant Nonverbal Behaviors and Impact of Personality Traits. 7th International Conference on Affective Computing and Intelligent Interaction (ACII2017), Oct 2017, San Antonio, Texas, United States. ⟨10.1109/ACII.2017.8273624⟩. ⟨hal-01660059⟩