Key result
Personalized machine learning from chest-worn sensors detects stress with ~100% accuracy, beating population-level models.
Why the study?
Stress detection is crucial for individual well-being, motivating the investigation of stress detection using machine learning from chest-worn sensor data.
Do personalized machine learning models using chest-worn sensor data improve stress detection accuracy compared to population-level models?
Do personalized machine learning models using chest-worn sensor data improve stress detection accuracy compared to population-level models?
Absolute Event Rate: 99.95% vs 99.71%
Personalized machine learning models applied to chest-worn sensor data can detect physiological stress with near-perfect accuracy, outperforming generalized population-level models.
Promising for wearable stress monitoring research; leaves open generalizability and clinical utility in larger cohorts.
Stress detection is crucial for individual well-being. We investigated stress detection using machine learning (ML) from chest-worn sensor data. We used the WESAD dataset that recorded physiological data via a chest-worn sensor when 15 subjects were in stress and non-stress. We empirically evaluated different combinations of window sizes and ML models for stress detection. Our analysis shows XGBoost achieved the highest accuracy of 99.71%, F1-score 99.57%, and area under the curve (AUC) 99.61% in detecting population-level stress with an optimal window size of 500 ms. We also developed personalized stress detection models using the same window size. Subject-level results show stress can be more accurately detected (accuracy 99.95%, F1-score 99.90%, and AUC 99.87%) with personalized models. Our findings also demonstrate a 2% improvement in stress detection compared to prior studies. This work could prove beneficial for quick stress detection and understanding the cause of stress.
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Hasan et al. (2024) studied Stress (n=15). Personalized machine learning models (XGBoost) using chest-worn sensor data vs. Population-level models was evaluated on Stress detection accuracy. Personalized machine learning models using chest-worn sensor data detected stress with 99.95% accuracy, improving upon population-level models (99.71% accuracy).
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