An autonomous wearable system for diurnal sweat biomarker data acquisition. Issue 24 (14th October 2020)

• Record Type: Journal Article
• Authors: Hojaiji, Hannaneh; Zhao, Yichao; Gong, Max C.; Mallajosyula, Mudith; Tan, Jiawei; Lin, Haisong; Hojaiji, Amir M.; Lin, Shuyu; Milla, Carlos; Madni, Asad M.; Emaminejad, Sam
• Is Part Of: Lab on a chip. Volume 20:Issue 24(2020)
• Page Start: 4582
• Page End: 4591
• Publication Date: 2020-10-14
• Subjects: Miniature electronic equipment -- Periodicals; Combinatorial chemistry -- Periodicals; Biotechnology -- Periodicals; 543.0813
• DOI: 10.1039/d0lc00820f
• Languages: English
• ISSNs: 1473-0197

Abstract

Abstract : We present a hybrid-flex wearable system to autonomously analyze sweat—induced at programmable secretion rates—for diurnal biomarker data acquisition. Abstract : To track dynamically varying and physiologically relevant biomarker profiles in sweat, autonomous wearable platforms are required to periodically sample and analyze sweat with minimal or no user intervention. Previously reported sweat sensors are functionally limited to capturing biomarker information at one time-point/period, thereby necessitating repeated user intervention to increase the temporal granularity of biomarker data. Accordingly, we present a compact multi-compartment wearable system, where each compartment can be activated to autonomously induce/modulate sweat secretion ( via iontophoretic actuation) and analyze sweat at set time points. This system was developed following a hybrid-flex design and a vertical integration scheme—integrating the required functional modules: miniaturized iontophoresis interfaces, adhesive thin film microfluidic-sensing module, and control/readout electronics. The system was deployed in a human subject study to track the diurnal variation of sweat glucose levels in relation to the daily food intake. The demonstrated autonomous operation for diurnal sweat biomarker data acquisition illustrates the system's suitability for large-scale and longitudinal personal health monitoring applications.