A Wrist System for Daily Stress Monitoring Using Mid-Level Physiological Fusion and Late Fusion with Survey-Based Labels

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.