Forecasting Human Core and Skin Temperatures: A Long-Term Series Approach

Abstract

Human core and skin temperature (Tcr and Tsk) are crucial indicators of human health and are commonly utilized in diagnosing various types of diseases. This study presents a deep learning model that combines a long-term series forecasting method with transfer learning techniques, capable of making precise, personalized predictions of Tcr and Tsk in high-temperature environments with only a small corpus of actual training data. To practically validate the model, field experiments were conducted in complex environments, and a thorough analysis of the effects of three diverse training strategies on the overall performance of the model was performed. The comparative analysis revealed that the optimized training method significantly improved prediction accuracy for forecasts extending up to 10 min into the future. Specifically, the approach of pretraining the model on in-distribution samples followed by fine-tuning markedly outperformed other methods in terms of prediction accuracy, with a prediction error for Tcr within ±0.14 °C and Tsk, mean within ±0.46 °C. This study provides a viable approach for the precise, real-time prediction of Tcr and Tsk, offering substantial support for advancing early warning research of human thermal health.