Stress Detection Using Wearable Physiological and Sociometric Sensors

• URL: http://arxiv.org/abs/2604.12746v1
• Date: Tue, 14 Apr 2026 13:58:14 GMT
• Title: Stress Detection Using Wearable Physiological and Sociometric Sensors
• Abstract summary: This paper presents a machine learning approach for the automatic detection of stress of people in a social situation.<n>By combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations.
• Score: 1.7227144264575978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and k-nearest neighbor. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real-time stress detection. Finally, we present an study of the most discriminative features for stress detection.

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