Inpainting for artifact restoration in rPPG signals: a tool for real-world applicability in wellness and driver monitoring systems
Year: 2026
Type: article
Abstract: Objective. Photoplethysmography (PPG) and remote PPG (rPPG) are widely used non-invasive techniques for monitoring cardiovascular parameters. However, signal artifacts from motion, lighting variations, and environmental noise pose significant challenges for accurate physiological measurement, particularly in non-contact rPPG systems. To address these issues, we propose a novel generative inpainting framework designed to restore corrupted segments of PPG and rPPG signals. Approach. Our method leverages a large-scale synthetic dataset that spans a broad range of heart rates (30–180 bpm) and incorporates diverse artifact profiles to simulate real-world conditions. The inpainting model is built upon a custom Wasserstein GAN architecture, using a gradient penalty to ensure stable adversarial training. This selective reconstruction approach targets only the corrupted segments while preserving the integrity of high-quality signal portions. Main results. Results show that the proposed framework improves signal quality compared to corrupted signals. For synthetic datasets spanning heart rates from 30 to 180 bpm, signal-to-noise ratio increases from approximately −0.65 to −0.20 dB to 3.27–4.16 dB after inpainting, while the mean absolute error decreases from 0.08–0.09 to 0.05–0.06. Feature-level similarity also improves, with Fréchet Encoder Distance reduced from 0.12 to 0.03 for real PPG and from 0.07 to 0.01 for real rPPG, and consistent reductions observed across all synthetic heart-rate ranges (from 0.23–0.47 to 0.01–0.04). Heart-rate estimates derived from the reconstructed signals are statistically equivalent to those obtained from clean references. Significance. The proposed generative inpainting framework effectively restores degraded PPG and rPPG signals and preserves heart-rate estimates, supporting its use in non-critical physiological monitoring applications such as wellness monitoring and automotive contexts. Validation on real data was limited to relatively clean, resting-state recordings; further studies are required to assess performance under high-motion and real-world conditions.
Source: Physiological Measurement
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,325
OpenAlex ID: W7154576872