Abstract
A sedentary lifestyle is one of the leading risk factors for developing obesity, increasing the risk of cardiovascular disease. In this work, we developed a novel approach to tracking glucose and cortisol to observe the dynamic relationship between the target biomarkers due to physical activity, nutrition, and the circadian cycle, via a machine learning-assisted wearable sweat-based electrochemical sensor. The machine learning approach helps to provide real-time biomarker value and a basis for actionable insight. Through this observational study, we illustrate the real-world performance of our sensing platform by examining the glucose and cortisol levels in passive sweat (<1 μL/min) of 4 healthy participants over 3 weeks for approximately 24–30 h twice a week. The ensemble learning model reached an R2 of 0.96 with an RMSE of 0.4. This novel glucose and cortisol tracking paradigm establishes the foundation for future research, including lifestyle choices’ effect on target biomarkers of metabolic disorders.
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