A wearable sensor system measuring physiological signals achieved a consistent classification accuracy of almost 80% between stress and non-stress conditions.
Cross-Sectional
Early mental stress detection can prevent many stress related health problems. This study aimed at using a wearable sensor system to measure physiological signals and detect mental stress. Three different stress conditions were presented to a healthy subject group. During the procedure, ECG, respiration, skin conductance, and EMG of the trapezius muscles were recorded. In total, 19 physiological features were calculated from these signals. After normalization of the feature values and analysis of correlations among these features, a subset of 9 features was selected for further analysis. Principal component analysis reduced these 9 features to 7 principal components (PCs). Using these PCs and different classifiers, a consistent classification accuracy between stress and non stress conditions of almost 80% was found. This suggests that a promising feature subset was found for future development of a personalized stress monitor.
Wijsman et al. (Mon,) conducted a cross-sectional in Mental stress. Wearable physiological sensors vs. Non-stress conditions was evaluated on Classification accuracy between stress and non-stress conditions. A wearable sensor system measuring physiological signals achieved a consistent classification accuracy of almost 80% between stress and non-stress conditions.
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