Signal processing and machine learning approaches for drinking behavior detection using PPG waveforms

Adequate hydration is essential for cardiovascular stability, thermoregulation and cognitive performance, yet current assessment methods are invasive or laboratory-dependent, limiting their use for continuous monitoring. This study proposes a noninvasive approach for classifying drinking behavior using photoplethysmography (PPG)-derived waveform features. PPG signals from 155 participants were collected under standardized conditions and grouped by self-reported fluid intake (2–4, 4–6 and >6 cups). Fiducial-point-based timing intervals, amplitude ratios and morphological descriptors were extracted to quantify cardiovascular changes. To address class imbalance, up-sampling, synthetic minority over-sampling technique (SMOTE), generative adversarial network-based feature synthesis with physiological consistency checks and image-based augmentation were applied. Model performance, evaluated using an 80:20 stratified hold-out repeated across ten runs, achieved high accuracy with up-sampling and image-based augmentation (97% and 91%; AUC ≈ 1.00 and 0.99), while SMOTE performed comparably (AUC 0.95). Kruskal–Wallis analysis confirmed significant hydration-related differences in time-domain (e.g. systolic-to-diastolic ratio) and amplitude-domain indices (e.g. pulse amplitude index), with small-to-moderate effect sizes. These results demonstrate that PPG features can capture hydration-related cardiovascular variability and support behavioral classification. The framework offers a foundation for wearable, real-time hydration monitoring and future integration with objective intake measures and multi-site PPG acquisition.