Stress Detection Using Wearable Physiological and Sociometric Sensors
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915936679755776 |
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| author | Mozos, Oscar Martinez Sandulescu, Virginia Andrews, Sally Ellis, David Bellotto, Nicola Dobrescu, Radu Ferrandez, Jose Manuel |
| author_facet | Mozos, Oscar Martinez Sandulescu, Virginia Andrews, Sally Ellis, David Bellotto, Nicola Dobrescu, Radu Ferrandez, Jose Manuel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12746 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Stress Detection Using Wearable Physiological and Sociometric Sensors Mozos, Oscar Martinez Sandulescu, Virginia Andrews, Sally Ellis, David Bellotto, Nicola Dobrescu, Radu Ferrandez, Jose Manuel Machine Learning Signal Processing 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. |
| title | Stress Detection Using Wearable Physiological and Sociometric Sensors |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2604.12746 |