Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2025

Abstract

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.

Keywords

Photoplethysmography, Heart rate variability, Morphology-aware modeling

Discipline

Numerical Analysis and Scientific Computing | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Areas of Excellence

Economics of Ageing and Healthcare Management

Publication

UbiComp Companion '25: 11th Workshop on Body-Centric Computing Systems (BodySys), co-located with UbiComp,

First Page

745

Last Page

750

ISBN

9798400714771

Identifier

10.1145/3714394.3756171

Publisher

ACM

City or Country

New York

Embargo Period

10-15-2025

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://doi.org/10.1145/3714394.3756171

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