Machine learning-powered wearable interface for distinguishable and predictable sweat sensing.

The constrained resources on wearable devices pose a challenge in meeting the demands for comprehensive sensing information, and current wearable non-enzymatic sensors face difficulties in achieving specific detection in biofluids. To address this issue, we have developed a highly selective non-enzymatic sweat sensor that seamlessly integrates with machine learning, ensuring reliable sensing and physiological monitoring of sweat biomarkers during exercise. The sensor consists of two electrodes supported by a microsystem that incorporates signal processing and wireless communication.