Stress Detection Using Wearable Physiological and Sociometric Sensors

2026-04-14

AI Summary

Accurate recognition of stress in social contexts is crucial for individual well-being. This work proposes a machine learning approach that integrates dual-modal sensing data—wearable physiological signals and sociometric measurements—to automatically detect social stress states, representing the first systematic effort to jointly leverage these two data sources for this purpose. Using classifiers such as support vector machines, AdaBoost, and k-nearest neighbors, the study identifies and analyzes the most discriminative multimodal features. Evaluated on the Trier Social Stress Test, the proposed method effectively distinguishes between stressed and neutral states, significantly outperforming unimodal approaches and thereby demonstrating the efficacy and practical value of bimodal fusion for real-time stress recognition.

Technology Category

• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

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.

Problem

• stress detection
• wearable sensors
• physiological signals
• sociometric sensors
• social stress

Innovation

• multimodal sensor fusion
• stress detection
• wearable sensors
• machine learning
• Trier Social Stress Test