A Context-Aware Framework for Optimal Filter Parameter Selection: Enhancing PRV Estimation in Wearable Wrist PPG Signals

Wearable biosensing allows for continuous monitoring and intervention in daily settings. Wrist photoplethysmography (PPG) is a commonly used method for measuring cardiac activity ambulatorily. A typical data preprocessing step is to apply a fixed, one-size-fits-all band-pass filter before peak detection and pulse rate variability (PRV) calculation. However, our analyses reveal that fixed filtering can cause significant errors in PRV estimation even with minimal motion artifacts, and the best filter settings differ across individuals and contextual states. Based on these findings, we introduce a person- and context-aware framework to adaptively select filter parameters at a window level. We instantiate this framework using models that gate segments by signal quality and select band-pass cutoff frequencies for each window. In tests across three datasets, our adaptive method improved PRV (RMSSD) accuracy by up to 279ms compared to a fixed filter and enhanced stress detection. It also increased the amount of usable inter-beat intervals without losing accuracy. Our collective results suggest shifting focus from solely removing motion artifacts toward adaptive selection of filter configurations that considers who is being measured and their state.