Novel Method for Real-Time Human Core Temperature Estimation using Extended Kalman Filter

• Type: Other Publications
• Date: 2024

Authors

Aslani, R (Author) Other; Dias, D (Author) Other; Coca, A (Author) Other; Cunha, JPS (Author) FEUP

Other information

Authenticus ID: P-011-5F9

DOI: 10.36227/techrxiv.170774654.44310272/v1

Abstract (EN): <jats:p id="p1">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.</jats:p>

Language: English

Type (Professor's evaluation): Scientific