Morphological Photoplethysmography Features Enhance Stress Detection in Earbud Sensors

Stress monitoring has become a focal interest in health sensing due to the mental and physical effects of long-term stress. Recent work demonstrated the feasibility of photoplethysmography (PPG)-based heart rate variability (HRV) features from earbud sensors to detect stress. However, morphological PPG features from earbuds have not been evaluated for stress detection. We analyzed physiological data from periods of stress and non-stress for 97 subjects. We trained machine learning models on PPG morphological features and HRV features from the earbuds, as well as ECG HRV features from a reference device. The morphological features (F1 score: 0.879) outperformed PPG HRV features (F1 score: 0.773) in stress classification. The combination of PPG morphological features and HRV features (F1 score: 0.880) performed similarly to ECG HRV features (F1 score: 0.887; ∆F1% = 0.798%). The results suggest earbud PPG morphological and HRV features can detect stress with similar fidelity to ECG, despite the smaller form factor and limited sampling rate. Thus, earbud sensors may be a strong candidate for stress monitoring in physiology due to their user-friendly and comfortable nature.