融合生理与社交传感器，实现社交压力情境下的精准应力检测
Stress Detection Using Wearable Physiological and Sociometric Sensors
- 结合可穿戴生理与社交传感器数据进行机器学习分类
- 在TSST测试中准确区分压力与平静状态，准确率显著提升
- 适合心理健康监测、人机交互等实时场景应用
压力是现代社会中个体面临的重要问题。本文提出一种基于机器学习的自动应力检测方法，通过融合捕捉生理反应和社交行为的两种可穿戴传感器系统，在受控的提里社交压力测试（TSST）中实现对压力与中性状态的有效区分。我们比较了支持向量机、AdaBoost和K近邻等多种分类器性能。实验结果表明，联合使用两类传感器可显著提升识别准确率。同时，论文单独评估了每种传感器模态的判别能力，并分析了最具有判别力的特征。研究为实时压力监测提供了可行的技术路径。
原文摘要 · Abstract (English)
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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