Key result
Wearable sensors and machine learning accurately discriminate stressful from neutral situations during controlled stress tests.
Why the study?
Does combining physiological and sociometric wearable sensors improve the automatic detection of stress in social situations?
Does combining physiological and sociometric wearable sensors improve the automatic detection of stress in social situations?
Combining physiological and sociometric wearable sensors with machine learning classifiers enables accurate discrimination of stress in social situations.
Hypothesis-generating for wearable stress detection; leaves open real-world validation before clinical use.
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 [Formula: see text]-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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Mozos et al. (2016) studied Stress. Wearable physiological and sociometric sensors with machine learning vs. Individual sensor modalities was evaluated on Discrimination between stressful and neutral situations during a controlled Trier social stress test (TSST). Combining wearable physiological and sociometric sensors with machine learning accurately discriminated between stressful and neutral situations during a controlled Trier social stress test.
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