Core body temperature estimation from heart rate via multi-model Kalman filtering and variance-based fusion

Journal article

Objective. Accurate and non-invasive estimation of core body temperature (CBT) is essential for preventing heat-related illnesses during physical activity and thermal stress. The objective of this work is to develop and evaluate a framework for real-time CBT estimation using only heart rate (HR) data, enabling a lightweight solution suitable for deployment on wearable devices. Approach. We propose a multi-model Kalman filtering (KF) framework with variance-based fusion. Two variants were developed: a supervised Physiological State-Specific KF (PSSK) that uses activity labels (rest, exercise, recovery) to train distinct models, and an unsupervised trial clustering-based KF (TCBK) that clusters trials based on HR–CBT features to capture latent physiological variability without state annotations. Both models were evaluated on two independent datasets and compared against baseline methods. Main results. In within-dataset evaluations, TCBK achieved the highest accuracy with a root mean square error (RMSE) of 0.38 ∘C (Dataset 1) and 0.41 ∘C (Dataset 2). In cross-dataset generalization, PSSK demonstrated superior robustness with an RMSE of 0.88 ∘C, whereas the TCBK model’s error increased to 1.56 ∘C. Both proposed models outperformed the established Buller and Falcone models. Significance. This work demonstrates that lightweight, HR-only models can provide accurate CBT estimation by incorporating state- or context-aware modeling. The framework offers a practical and deployable solution for continuous thermal strain monitoring in occupational and athletic settings, providing a balance between performance and real-world applicability for wearable technology.

Authors: Zhao, Y; HM Bergmann, J

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IOP Publishing
• Journal: Physiological Measurement
• Volume: 46
• Issue: 10
• Article number: 105002
• Publication date: 2025-10-14
• Acceptance date: 2025-10-01
• DOI: 10.1088/1361-6579/ae0efd
• EISSN: 1361-6579
• ISSN: 0967-3334
• Language: English
• Keywords: heart rate; body temperature; core temperature estimation; Kalman filter; core temperature