Rapid Dataset-Free Self-Calibration for Heat Flux-Based Core Body Temperature Sensors

This study presents a reference-free calibration method for wearable core body temperature sensors based on the single-heat-flux approach, eliminating the need for a pre-constructed dataset and reducing the required calibration time. In conventional single-heat-flux methods, calibration relies on invasive measurements or numerically generated datasets and typically requires at least 1 h, posing practical challenges. In contrast, the proposed method identifies calibration parameters directly from measured temperature data within approximately 1500 s, which is much quicker than the 3600 s required by existing methods. We validated our method through numerical simulations considering factors such as skin thickness and acceptable measurement errors. These simulations confirmed that the proposed approach reliably achieves adequate calibration without prior dataset construction. Furthermore, we conducted a human subject experiment under resting conditions, where ambient and core body temperature fluctuations, along with heat tolerance responses, were minimal. The results demonstrate that our method can achieve clinically sufficient accuracy in estimating core body temperature, even in the presence of environmental perturbations such as introduced airflow. While the proposed method currently assumes stable conditions and may face challenges in highly dynamic environments involving intense physical activity or significant thermal changes, it marks a critical step toward more accessible and practical wearable core body temperature monitoring. Future work will focus on validating the method with a larger cohort of subjects and integrating additional physiological sensors to enhance reliability and broaden applicability.