Kalman Filter Models for the Prediction of Individualised Thermal Work Strain

Abstract

It is important to monitor and assess the physiological strain of individuals working in hot environments to avoid heat illness and performance degradation. The body core temperature (Tc) is a reliable indicator of thermal work strain. However, measuring Tc is invasive and often inconvenient and impractical for real-time monitoring of workers in high heat strain environments. Seeking a better solution, the main aim of the present study was to investigate the Kalman filter method to enable the estimation of heat strain from non-invasive measurements (heart rate (HR) and chest skin temperature (ST)) obtained 'online' via wearable body sensors. In particular, we developed two Kalman filter models. First, an extended Kalman filter (EFK) was implemented in a cubic state space modelling framework (HR versus Tc) with a stage-wise, autoregressive exogenous model (incorporating HR and ST) as the time update model. Under the second model, the online Kalman filter (OFK) approach builds up thetimeupdateequationdependingonlyontheinitialvalueofTcandthelatestvalueofthe exogenous variables. Both models were trained and validated using data from laboratory-and outfield-based heat strain profiling studies in which subjects performed a high intensity military foot march. While both the EKF and OKF models provided satisfactory estimates of Tc, the results showed an overall superior performance of the OKF model (overall root mean square error, RMSE = 0.31 C) compared to the EKF model (RMSE = 0.45 C).