Online Prediction of Core Body Temperature from Sweat Wearable Printed Sensors Using Recurrent Neural Network

Real-time monitoring of core body temperature (CBT) is important for preventing heat-related physiological problems during work or exercise. This paper shows how sweat biomarkers (sodium and potassium concentrations) as measured by printed sensors on a sweat wearable patch can be used for online continual prediction of CBT. These sensor measurements and additional biomarker data (heart rate and regional sweat rate) were collected from two healthy male athletes during controlled cycling sessions. The biomarker data was used to continuously predict CBT with one of three models: a recurrent neural network (RNN), a multilayer perceptron, and a linear regression model. The results show that with a window size of 30 seconds, sweat sodium and potassium concentrations outperform other biomarker pairs in predicting CBT. Out of all three models, an RNN model with only 70.8 K parameters achieved the lowest prediction error of 0.04 °C using the two sweat biomarkers. These findings support the use of sweat-based non-invasive monitoring systems for reliable online CBT prediction.