A Data-Driven Biophotonic Skin Model for Wearable Sensing

Accurate modeling of light-matter interactions in biological tissue is fundamental to the advancement of biophotonic sensing, in-vivo diagnostics, and the development of next-generation wearable health monitors. While computational methods such as Monte Carlo simulations are powerful tools for virtual prototyping, the reliability of their predictions is strictly limited by the fidelity of the underlying skin model. Current industry-standard models primarily rely on optical parameters derived from ex-vivo tissue samples or dataset with limited size and variabilities in measurement methods, often failing to capture the complex spectral responses and inter-subject variability found in living populations. To address this limitation, we present a data-driven biophotonic skin model rigorously calibrated and validated against extensive in-vivo dataset. Our study utilizes smartwatch data acquired across a broad spectral range (UV to SWIR) and multiple sensor geometries, with subjects covering a diverse range of skin tones. By grounding the standard multilayer skin architecture in experimental data, this work provides a robust, statistically validated foundation for the design, simulation, and optimization of future wearable optical sensors.