Person-specific behavioural features for automatic stress detection

Abstract : This paper introduces behavioural features for automatic stress detection, and a person-specific normalization to enhance the performance of our system. The presented features are all visual cues automatically extracted using video processing and depth data. In order to collect the necessary data, we conducted a lab study for stress elicitation using a time constrained arithmetic mental test. Then, we propose a set of body language features for stress detection. Experimental results using a SVM show that our model can detect stress with high accuracy (77%). Moreover, person specific normalization significantly improves classification results (from 67% to 77%). Also, the performance of each of the presented features is discussed.
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Jonathan Aigrain, Séverine Dubuisson, Marcin Detyniecki, Mohamed Chetouani. Person-specific behavioural features for automatic stress detection. IEEE Conference on Automatic Face and Gesture Recognition, May 2015, Ljubljana, Slovenia. ⟨10.1109/FG.2015.7284844⟩. ⟨hal-01364098⟩

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