Quantitative evaluation of skin pigmentation effects on photoplethysmography using vascular finger phantoms and Monte Carlo simulation.

SIGNIFICANCE: Photoplethysmography (PPG) underpins a wide range of clinical and consumer optical monitoring technologies. However, differences in skin pigmentation can alter light-tissue interactions through increased absorption by melanin, affecting PPG signal quality and potentially contributing to performance disparities across populations. AIM: This study aimed to investigate the effect of skin pigmentation on PPG signal quality by combining an advanced vascular finger phantom with Monte Carlo simulations to directly link experimentally measured PPG features to underlying photon transport behavior in both reflectance and transmittance measurement modes. APPROACH: A multilayer vascular finger phantom incorporating a three-level, physiologically inspired vascular network and interchangeable skin layers representing pale, medium, and dark skin pigmentation was developed. The optical properties were characterized across 530, 665, and 940 nm. PPG signals were acquired in reflectance and transmittance modes under controlled pulsatile flow conditions. Quantitative signal metrics, including peak-to-peak amplitude, signal-to-noise ratio (SNR), and AC/DC ratio, were extracted. Monte Carlo simulations were implemented using the measured optical properties to model photon transport, energy deposition, and depth-dependent absorption. RESULTS: Increasing skin pigmentation resulted in systematic reduction in all PPG features across all wavelengths, with the strongest attenuation observed at shorter wavelengths. Monte Carlo simulations revealed increased superficial absorption and reduced photon penetration in darker skin layers, closely matching experimental trends. CONCLUSIONS: This combined experimental-computational framework provides quantitative and mechanistic insight into how skin pigmentation influences light-tissue interaction and PPG signal quality, with direct relevance to emerging wearables and PPG-based applications, where signal quality disparities across skin tones may impact algorithm performance.