A Fully Integrated Wearable Sweat Sensing Patch for Online Analysis of Multiple Parkinson's Disease‐Related Biomarkers
Hao Zhao, Bai Jing, Xieli Zhang, Dan Li, Chang Qi, Yong Xia, Yan Liu, Samuel M. Mugo, Hongda Wang, Qiang Zhang
Abstract
Parkinson's disease (PD) is marked by a prolonged asymptomatic "window period" (several years). Early prediction and diagnosis during this window are crucial, as timely interventions can slow disease progression. In this study, a fully integrated wearable sweat-sensing patch capable of real-time detection of three key PD biomarkers: L-Dopa, ascorbic acid, and glucose is developed. The system includes a biomimetic microfluidic module for sedentary sweat collection, an advanced electrochemical sensing platform for biomarker analysis, on-site signal processing circuitry for data management, and custom software for real-time data visualization. A universal strategy is proposed to significantly extend the stability of oxidase enzymes without activity loss, achieved through the design of Cu-oxidase hybrid nanoflowers. The patch is successfully tested on dozens of volunteers (healthy and PD patients in various stages), demonstrating its capability to monitor biomarkers in real time, assess PD progression, and optimize medication management.