Toward automatic detection of acute stress: Relevant nonverbal behaviors and impact of personality traits

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.

[1]  Zhiwei Zhu,et al.  A Real-Time Human Stress Monitoring System Using Dynamic Bayesian Network , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[2]  S. Folkman,et al.  Stress, appraisal, and coping , 1974 .

[3]  Tim M. den Uyl,et al.  Automated facial coding: validation of basic emotions and FACS AUs in FaceReader , 2014 .

[4]  Gabriel M Khan.  A new electrode placement method for obtaining 12-lead ECGs , 2015, Open Heart.

[5]  A. Barreto,et al.  Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Georgios N. Yannakakis,et al.  To rank or to classify? Annotating stress for reliable PTSD profiling , 2015, 2015 International Conference on Affective Computing and Intelligent Interaction (ACII).

[7]  Koichi Yamada,et al.  Evaluating Instantaneous Psychological Stress from Emotional Composition of a Facial Expression , 2013, J. Adv. Comput. Intell. Intell. Informatics.

[8]  M. Kivimäki,et al.  Work stress in the etiology of coronary heart disease--a meta-analysis. , 2006, Scandinavian journal of work, environment & health.

[9]  Claire Médigue,et al.  Heart rate variability during exercise performed below and above ventilatory threshold. , 2004, Medicine and science in sports and exercise.

[10]  Akane Sano,et al.  Predicting students' happiness from physiology, phone, mobility, and behavioral data , 2015, 2015 International Conference on Affective Computing and Intelligent Interaction (ACII).

[11]  H. Wallbott.  Bodily expression of emotion , 1998 .

[12]  Dimitris N. Metaxas,et al.  Image-Based Stress Recognition Using a Model-Based Dynamic Face Tracking System , 2004, International Conference on Computational Science.

[13]  Pablo Laguna,et al.  Analysis of heart rate variability during exercise stress testing using respiratory information , 2010, Biomed. Signal Process. Control..

[14]  S. Dickerson,et al.  Acute stressors and cortisol responses: a theoretical integration and synthesis of laboratory research. , 2004, Psychological bulletin.

[15]  Munna Khan,et al.  Vocal Indicators of Emotional Stress , 2015 .

[16]  Mohamed Chetouani,et al.  Multimodal Stress Detection from Multiple Assessments , 2018, IEEE Transactions on Affective Computing.

[17]  Marco Wiering,et al.  A Model Based Method for Automatic Facial Expression Recognition , 2005, ECML.

[18]  C. Kirschbaum,et al.  The 'Trier Social Stress Test'--a tool for investigating psychobiological stress responses in a laboratory setting. , 1993, Neuropsychobiology.

[19]  M. Knapp,et al.  Nonverbal communication in human interaction , 1972 .

[20]  Jean-Philippe Thiran,et al.  Detecting emotional stress from facial expressions for driving safety , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[21]  Jean-Claude Martin,et al.  Assessing Postural Control for Affect Recognition Using Video and Force Plates , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[22]  Jean-Claude Martin,et al.  Multimodal Expressions of Stress during a Public Speaking Task: Collection, Annotation and Global Analyses , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[23]  Jean-Claude Martin,et al.  Video analysis of approach-avoidance behaviors of teenagers speaking with virtual agents , 2013, ICMI '13.

[24]  Christian Réveillère,et al.  Validation par analyse factorielle du Big Five Inventory français (BFI-Fr). Analyse convergente avec le NEO-PI-R , 2010 .

[25]  P. Ekman,et al.  Facial action coding system , 2019 .

[26]  James W. Davis,et al.  The representation and recognition of human movement using temporal templates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[27]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[28]  Alireza Yousefi,et al.  Management of Stress and Anxiety Among PhD Students During Thesis Writing: A Qualitative Study , 2016, The health care manager.

[29]  Aneta Pavlenko.  The Verbal Communication of Emotions: Interdisciplinary Perspectives. , 2019, (Re)constructing Gender in a New Voice.

[30]  M. Dolores Castillo,et al.  Coping with stress and its relationship with personality dimensions, anxiety, and depression , 2010 .

[31]  Mohamed Chetouani,et al.  Person-specific behavioural features for automatic stress detection , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[32]  Tobias Baur,et al.  The social signal interpretation (SSI) framework: multimodal signal processing and recognition in real-time , 2013, ACM Multimedia.