physics-informed machine learning

Researchers publishing in Nature have developed a physics-informed machine learning approach for calibrating wearable electrochemical sweat biosensors that monitor metabolites. The method combines physical models with machine learning to achieve robust calibration and physiological validation, potentially making sweat-based wearable health monitoring more reliable for continuous metabolite tracking.