Distinguish self- and hetero-perceived stress through behavioral imaging and physiological features

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Stress reactivity is a complex phenomenon associated to multiple and multimodal expressions. Response to stressors has an obvious survival function and may be seen as an internal regulation to adapt to threat or danger. The intensity of this internal response can be assessed as the self-perception of the stress response. In species with social organization, this response also serves a communicative function, so-called hetero-perception. Our study presents multimodal stress detection assessment - a new methodology combining behavioral imaging and physiological monitoring for analyzing stress from these two perspectives. The system is based on automatic extraction of 39 behavioral (2D + 3D video recording) and 62 physiological (Nexus-10 recording) features during a socially evaluated mental arithmetic test. The analysis with machine learning techniques for automatic classification using Support Vector Machine (SVM) show that self-perception and hetero-perception of social stress are both close but different phenomena: self-perception was significantly correlated with hetero-perception but significantly differed from it. Also, assessing stress with SVM through multimodality gave excellent classification results (F1 score values: 0.9 ± 0.012 for hetero-perception and 0.87 ± 0.021 for self-perception). In the best selected feature subsets, we found some common behavioral and physiological features that allow classification of both self- and hetero-perceived stress. However, we also found the contributing features for automatic classifications had opposite distributions: self-perception classification was mainly based on physiological features and hetero-perception was mainly based on behavioral features.HighlightsWe investigated self- and hetero-perceived stress in 25 young adults during a socially evaluated mental arithmetic test.We present a new methodology combining behavioral imaging and physiological monitoring, with machine learning method to assess social stress and its main components.Self-perception of social stress (assessed by the subjects themselves) was significantly correlated with hetero-perception (assessed by external observers)Classification methods allow to associate self perception to mainly physiological features and hetero-perception to mainly behavioral features.We discuss the implication of the specifics of behavioral and physiological features to support internal perception or inter-individual communication from an evolutionary perspective.

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