Robust PPG-Exclusive Heart Rate Estimation During Maximal Exercise Using Population-Based Priors

PPG is the main technology for wearable heart rate (HR) monitoring. However, motion artifacts during intense physical activity reduce its reliability due to spectral overlap with the cardiac signal. This study investigates whether severely contaminated PPG signals can yield reliable HR estimates without auxiliary sensors by leveraging prior knowledge of HR dynamics. We acquired simultaneous PPG and ECG signals from 21 subjects during a maximal cycloergometer test. Using Leave-One-Subject-Out cross-validation, we built HR evolution priors from the ground-truth ECG and applied Dynamic Time Warping (DTW) to align normalized HR curves and estimate a barycenter average. DTW aligns normalized HR curves with minimal error across all subjects, validating robust population-based priors. For each test subject, we combined the PPG Power Spectral Density (PSD) with the prior distribution to generate a "posterior PSD". We identified and ranked candidate HR trajectories using heuristic search within the "posterior PSD" and selected the most likely path. Our approach reduced mean absolute error (MAE) in BPM relative to the ground-truth ECG by a median of 63.3% (IQR: 47.3%-74.6%) compared to a PPG+accelerometer benchmark. These results show that physiological priors and DTW-based alignment enable robust, PPG-exclusive HR monitoring during high-intensity exercise.