Wearable sensors use sweat to identify and measure physiological conditions. Caltech’s Wei Gao, assistant professor of medical engineering, has devised an “electronic skin” that continuously monitors nine different markers that characterize a stress response.
By Cynthia Eller/Caltech
Those wearing this electronic skin — a small, thin adhesive worn on the wrist — are free to engage in all their normal daily activities with minimal interference, which allows for the measurement of both baseline and acute levels of stress.
Stress is a slippery concept. We talk about “feeling stressed” or a situation “being stressful,” and we may attach stress to physical symptoms: “I have a stress headache” or “I’m grinding my teeth at night. It must be stress.” The term stress can apply to all sorts of feelings, symptoms, behaviors, and experiences.
There is no single biomarker available to tell us definitively whether or how much a person is stressed. However, stress generates a constellation of bodily reactions that, taken together, can provide a measure of stress independent of self-reports. Gao and his team are monitoring this constellation with CARES (consolidated artificial-intelligence-reinforced electronic skin).
What’s New in CARES
“When a person is under stress, sweat becomes rich with metabolites like glucose, lactate, and uric acid, and electrolytes like sodium, potassium, and ammonium. What is new in CARES is that sweat sensors are integrated with sensors that record pulse waveforms, skin temperature, and galvanic skin response: physiological signals that also indicate stress in predictable ways.” Like previous sweat sensors, CARES can be battery powered and can wirelessly communicate with a phone or computer via Bluetooth.
The paper describing the CARES device, titled “A physicochemical sensing electronic skin for stress response monitoring,” appears in the January 19, 2024 issue of Nature Electronics. Co-authors are Assistant Professor of Medical Engineering Wei Gao, Changhao Xu (MS ’20), Yu Song, Juliane R. Sempionatto, Samuel R. Solomon (MS ’23), You Yu, Roland Yingjie Tay, Jiahong Li, Wenzheng Heng (MS ’23), Jihong Min (MS ’19), and Alison Lao of Caltech; Hnin Y. Y. Nyein of Hong Kong University of Science and Technology; and Tzung K. Hsiai and Jennifer A. Sumner of UCLA.
This article has been edited for clarity and brevity. You can read the entire article on caltech.edu.
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