Property-Specific Recoverability from Contact PPG to Camera rPPG under Heterogeneous Observation Conditions
2026-08-27 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors studied how well a camera-based method (rPPG) can capture detailed heart-related signals compared to a standard contact sensor (PPG). They found that while the overall heart rate from rPPG roughly matches the contact sensor, many detailed signal features do not reliably match from one recording to another. Differences were also influenced by skin tone, lighting, and motion, but not fully explained by signal quality. The study shows that just matching overall heart rate doesn’t guarantee that all important heart signal details are preserved in camera-based monitoring.
remote photoplethysmography (rPPG)photoplethysmography (PPG)heart rateCHROM methodsignal correlationLyapunov exponentFitzpatrick skin typemotion artifactslighting conditionsmachine learning calibration
Authors
Timothy Oladunni, Farouk Ganiyu-Adewumi
Abstract
Camera-derived remote photoplethysmography (rPPG) is commonly validated through endpoint accuracy, but endpoint performance does not establish whether other physiological properties of source contact photoplethysmography (PPG) remain preserved recording by recording. We evaluated property-specific PPG-to-rPPG recoverability on 655 recordings from the Multi-Domain Mobile Video Physiology Dataset using CHROM as a fixed camera-rPPG observation pathway. The pathway reproduced the published CHROM correlation regime, with heart-rate MAE of 15.26 bpm and Pearson correlation of 0.0801. Matched-versus-shuffled validation revealed modest recording-specific autocorrelation correspondence, while spectral and recurrence-rate measures showed little matched discrimination. Maximal Lyapunov exponents showed essentially no recording-specific PPG-to-rPPG correspondence, with correlation of 0.0231 and permutation p-value of 0.5584, despite population-level overlap. Endpoint discrepancy exhibited Fitzpatrick-associated heterogeneity after adjustment for lighting and motion, including a Fitzpatrick VI versus III contrast of 9.32 bpm, while dynamical discrepancy showed no corresponding gradient. Aggregate RGB signal-to-noise ratio did not materially account for the endpoint contrast. In subject-held-out analysis, adding motion and lighting consistently reduced MAE across linear, ridge, and random-forest learners relative to rPPG-HR-only calibration, with reductions up to 13.32 percent. These findings show that recoverability is property-specific: physiological properties differ in recording-specific preservation and dependence on observation conditions, and population-level plausibility does not establish preservation of individual recordings.