A new paradigm in tracking the dynamics of glucose and cortisol: An observational study from human sweat enabled by a skin sensor

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 (