Pre-Conference Talk by HU Changshou | Morphology-Aware HRV Estimation from Wrist PPG in Sedentary Scenarios

Photoplethysmography (PPG) is widely used in wearable devices for non-invasive heart rate variability (HRV) monitoring. While most prior work focuses on mitigating motion artifacts, recent studies highlight that even subtle contact pressure variations can distort waveform morphology and lead to inaccurate HRV estimates. In this work, we propose a morphology-aware deep learning framework that conditions HRV estimation on beat-level waveform types. Our model jointly encodes the raw PPG waveform and a sequence of pressure-induced morphology labels using parallel encoders, integrates them via cross-attention, and predicts normal-to-normal (NN) intervals and beat count to support downstream HRV computation. Evaluated on the public WF-PPG dataset, our method significantly improves estimation accuracy over pressure-agnostic baselines and narrows the gap toward clean finger PPG references. 

This is a Pre-Conference talk for 11th Workshop on Body-Centric Computing Systems (BodySys 2025).