Inflammatory Biomarker Analysis from Wearable Sweat Patches via Smartphone-Based Image Processing

Abstract

The detection of systemic inflammation through inflammatory biomarkers plays a critical role in identifying and managing pathological conditions. Conventional measurement of inflammatory biomarkers relies on invasive procedures such as blood sampling, which limits accessibility and requires frequent monitoring. Wearable sweat sensors offer a promising noninvasive alternative; however, robust interpretation of their visual signals remains challenging outside of laboratory environments. This study presents the first fully automated computational pipeline that translates colorimetric signals from a wearable sweat sensor into quantitative measurements of inflammatory biomarkers using smartphone-acquired images. The proposed approach enables reliable analysis under variable imaging conditions, supporting point-of-care (POC) inflammatory monitoring. Our results show that the pipeline significantly reduces measurement variability, achieving up to a 70% reduction in variability that may be induced by different lighting conditions. Additional experiments demonstrate robustness across different smartphones and image capture distances with end-to-end processing completed within a few seconds. Furthermore, validation using data from human participants with eczema demonstrates that the system can distinguish between healthy individuals and those exhibiting elevated inflammatory biomarker levels, with performance comparable to the gold-standard of enzyme-linked immunosorbent assay (ELISA). The complete pipeline was integrated into a mobile application, enabling near real-time analysis and supporting practical POC deployment.