Work

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

Year: 2026

Type: article

Abstract: 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.

Source: APSIPA Transactions on Signal and Information Processing

DOI: 10.1108/atsip-10-2025-0101