Person-specific behavioural features for automatic stress detection
Year: 2015
Type: conference-paper
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.
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W1664244372