The Internet of Things (IoT) possesses the potential to revolutionize the medical field. In order to spearhead an initiative to innovate diagnostic and therapeutic tools for each individual, bioelectronic devices with remote monitoring capabilities have quickly become attractive options. They can provide users avenues toward continuous tracking of health states and predictive analytics for early interventions of diseases. With this background, wearable sensors have been popular among us to monitor our physical wellness. On the market, noninvasive wearable devices can be used to track external metrics such as heart rates and electrocardiogram signals. However, they fail to inform users about elusive biomarkers at the molecular level, which possesses the potential to provide more insight into a person’s health situation. Traditionally, accessing bio-molecular information requires collecting blood or urine samples with invasive procedures or logistic complications. In this regard, sweat stands out as a great candidate because of its property of natural secretion and abundance in biomolecules, such as electrolytes, metabolites, xenobiotic molecules, and heavy metals. Hence, sweat is an ideal target to develop a wearable device that provides essential information regarding human physiology. Our project aims to demonstrate noninvasive and multiplexed sensing of biomolecules through interfacing printed circuit boards and electrode arrays. The advantage of this kind of sensor is that it enables real-time monitoring, requires small sample volume, and detects multiple biomolecules simultaneously. This platform allows for in-situ sweat collection, data processing, and bioinformatics transmission on a single circuit board without requiring extensive analyses or bulky instruments. Our work leverages a wearable sweat sensing platform towards noninvasive and continuous point-of-care health monitoring and management.
Dr. Li-Chia Tai received his PhD in Electrical Engineering and Computer Sciences from UC Berkeley. Subsequently, he was an Insight Health Data Science Fellow before joining the Department of Electrical and Computer Engineering at National Yang Ming Chiao Tung University (NYCU) in 2021. Dr. Tai’s research expertise spans across wearable sensors, biomedical devices, and artificial intelligence for health data analytics. In the past five years, he has published 15 papers in top-ranking journals, such as Advanced Materials, Science Advances, Nano Letters, ACS Nano, and ACS Sensors. He has developed wearable electronics to monitor pharmaceutical drugs for dosage optimization and other xenobiotic molecules for health analysis. He has integrated machine learning algorithms, optoelectronics, and microfluidics to classify circulating tumor cells for early cancer diagnosis. Dr. Tai is the recipient of UC Berkeley Departmental Fellowship and Fermilab Lee Teng Fellowship in Science and Engineering. He is also a finalist of Qualcomm Innovation Fellowship. He is interested in the intersection of bioelectronics and artificial intelligence toward personalized diagnostics and treatments. Dr. Tai’s research direction will explore the application of wearable sensor networks to gain insights into an individual’s health status, dosage optimization, and clinical implications.