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Novel Method for Real-Time Human Core Temperature Estimation using Extended Kalman Filter
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  • Rojan Aslani,
  • Duarte Dias,
  • Aitor Coca,
  • João Paulo Silva Cunha
Rojan Aslani

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Duarte Dias
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Aitor Coca
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João Paulo Silva Cunha
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The gold standard methods for real-time core temperature (CT) monitoring are invasive and cost-inefficient. The application of Kalman filters for an indirect estimation of CT has been explored in the literature since 2010. This paper presents a comparative study between different state of the art Extended Kalman Filter (EKF) estimation algorithms and a new approach based on a biomimetic human body response pre-emptive mapping concept. In this new method, a mapping model of the physiological response of the heart rate (HR) change to CT increase is pre-applied to the input of the EKF estimation CT procedure in a near real-time manner. The algorithm was trained and tested using two datasets (total participants = 18). The best performing algorithm with this novel pre-emptive mapping achieved in an average Root Mean Squared Error (RMSE) of 0.34°C while the best state of the art EKF model (without pre-emptive mapping) resulted in a RMSE of 0.41°C, leading to a 17% improvement performance of our novel method. Given these favorable outcomes, it is compelling to assess its efficacy on a larger dataset in the near future.

01 Feb 2024Submitted to TechRxiv
12 Feb 2024Published in TechRxiv