Why the study?
Wearable stress monitoring systems typically rely on supervised learning requiring labeled data, but self-reported stress labeling is subjective, prone to inaccuracies, and restricted to specific points in time.
Unsupervised machine learning classifiers offer a feasible, label-free alternative to supervised methods for continuous physiological stress monitoring in wearable devices.
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Labeling physiological signals remains a major barrier for supervised wearable stress monitors; leaves open unsupervised methods for clinical validation.
Iqbal et al. (2022) studied this question.
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