Paperdoi:10.3390/s26196105
PulsePath: Two-Stage Generative Waveform Refinement for Robust Remote Photoplethysmography Estimation
Sensors 2026, 26(19), 6105 (published 2026-09-26)
Licence: CC BY 4.0
TL;DR
Starts from the POS waveform instead of regressing the pulse from scratch. Stage A: conditional flow matching transports POS toward paired contact PPG, using a 6-channel multiscale spatiotemporal map plus a 25-channel recording-level frequency representation. Stage B: variance-preserving residual diffusion (velocity prediction) models what remains.
- Form factor: camera
Abstract
Remote photoplethysmography (rPPG) estimates pulse signals from facial videos, but motion, illumination variation, exposure control, and compression can distort the waveform used for heart-rate (HR) estimation. The Plane-Orthogonal-to-Skin (POS) waveform provides an observed physiological source that often preserves recording-specific periodic structure. Existing methods commonly regress the complete waveform directly or reconstruct it from a stochastic state without using this source as the starting point. We propose PulsePath, a two-stage framework that transports POS toward paired-contact photoplethysmography (PPG) and models only the residual remaining after deterministic inference. Stage A performs conditional Flow Matching (FM) from POS using a six-channel multiscale spatiotemporal map and a 25-channel recording-level frequency representation. Stage B fixes the deterministic FM estimate and reconstructs its remaining error with variance-preserving residual diffusion and velocity prediction. In intra-dataset testing, PulsePath achieves mean absolute error (MAE), root mean square error (RMSE), and Pearson correlation values of 0.46/0.94/0.99 on PURE and 0.29/0.80/0.99 on UBFC-rPPG. In cross-dataset testing, PulsePath achieves an MAE/RMSE of 0.23/0.63 when trained on UBFC-rPPG and tested on PURE, and 1.13/2.49 when trained on PURE and tested on UBFC-rPPG. The additional analyses show protocol-dependent component effects and training-run variability; the reported HR results do not establish waveform fidelity.
Verbatim from the publisher's DOI record (opens in a new tab) · Publisher licence: https://creativecommons.org/licenses/by/4.0/
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APA
Park, J., Oh, S., Lee, S. J., Engineering, D. O. E., & University, J. N. (2026). PulsePath: Two-Stage Generative Waveform Refinement for Robust Remote Photoplethysmography Estimation. Sensors 2026, 26, 6105. https://doi.org/10.3390/s26196105
BibTeX
@article{park2026pulsepath,
title = {{PulsePath: Two-Stage Generative Waveform Refinement for Robust Remote Photoplethysmography Estimation}},
author = {Park, Juha and Oh, Seungmin and Lee, Sang Jun and Engineering, Division of Electronic and University, Jeonbuk National},
year = {2026},
journal = {Sensors 2026, 26, 6105},
doi = {10.3390/s26196105}
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