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A Wrist System for Daily Stress Monitoring Using Mid-Level Physiological Fusion and Late Fusion with Survey-Based
Marija Simic1, Sravya Reddy Yammanuru1, Geraline Saguiafin1
1Signal Analysis Research (SAR) Group, Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University (TMU), 350 Victoria Street, Toronto, ON M5B 2K3, Canada.
Abstract:
Multi-sensor fusion can improve daily stress monitoring.
Methods:
A wrist-worn device includes a system of the galvanic skin response (GSR), PPG-derived heart rate variability (HRV), skin temperature, and SpO2, paired with self-reported questionnaires. The device streams data to a mobile app over Bluetooth Low Energy and updates the UI within 1-2 s. The physiological features are captured within a fixed window around each questionnaire time and undergo a mid-level fusion; late fusion is also evaluated using self-reports.
Results:
Against a commercial reference device, the proposed system achieved a mean absolute error of 0.23 for SpO2 and 4.94 for BPM in a one-day benchmark session. The system was validated through a technical evaluation using representative inputs and simulated survey labels. The fusion model was evaluated using simulated physiological and survey data. Using a support vector machine algorithm, a mean squared error of 0.08 was achieved when predicting simulated stress labels. Temperature was shown to have the strongest correlation with simulated stress levels at -0.43, followed by heart rate variability (HRV) at 0.36, while SpO2 had a negligible correlation at 0.09 in the current dataset.
Conclusion:
The system integrates multi-sensing, on-device preprocessing, BLE transmission, and a clear fusion workflow that creates a useful predictive performance of daily stress monitoring.
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