Tracking body core temperature in military thermal environments: An extended Kalman filter approach

Seng, Kok-Yong; Chen, Ying; Wang, Ting; Fun, David Chiok Yuen; Teo, Ya Shi; Tan, Pearl Min Sze; Ang, Wee Hon; Lee, Jason

doi:10.1109/bsn.2016.7516277

Cited by 9 publications

(11 citation statements)

References 9 publications

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“…Various research groups have published T C algorithms that meet field-established accuracy standards through the use of easily obtainable physiological measurements (e.g., heart rate and skin temperature) collected during physical activity [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. However, while model accuracy is high, many of these algorithms were trained and validated on datasets involving primarily young fit males [ 11 , 12 , 13 , 14 , 16 , 18 , 19 ], or only hot conditions [ 19 ], or with minimal data for ground truth T C ≥ 38.5 °C [ 10 , 14 , 16 , 19 ]—the temperatures above which heat injuries and illnesses are most prevalent, and thus accuracy is of utmost importance.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Algorithm to Non-Invasively Predict Core Body Temperature Using the Kenzen Wearable Device

Moyen¹,

Bapat²,

Tan

et al. 2021

IJERPH

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With climate change increasing global temperatures, more workers are exposed to hotter ambient temperatures that exacerbate risk for heat injury and illness. Continuously monitoring core body temperature (TC) can help workers avoid reaching unsafe TC. However, continuous TC measurements are currently cost-prohibitive or invasive for daily use. Here, we show that Kenzen’s wearable device can accurately predict TC compared to gold standard TC measurements (rectal probe or gastrointestinal pill). Data from four different studies (n = 52 trials; 27 unique subjects; >4000 min data) were used to develop and validate Kenzen’s machine learning TC algorithm, which uses subject’s real-time physiological data combined with baseline anthropometric data. We show Kenzen’s TC algorithm meets pre-established accuracy criteria compared to gold standard TC: mean absolute error = 0.25 °C, root mean squared error = 0.30 °C, Pearson r correlation = 0.94, standard error of the measurement = 0.18 °C, and mean bias = 0.07 °C. Overall, the Kenzen TC algorithm is accurate for a wide range of TC, environmental temperatures (13–43 °C), light to vigorous heart rate zones, and both biological sexes. To our knowledge, this is the first study demonstrating a wearable device can accurately predict TC in real-time, thus offering workers protection from heat injuries and illnesses.

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Section: Introductionmentioning

confidence: 99%

Accuracy of Algorithm to Non-Invasively Predict Core Body Temperature Using the Kenzen Wearable Device

Moyen¹,

Bapat²,

Tan

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…In this section, we describe the KF approaches proposed by Buller and his co-authors [13][14][15], as well as the EKF [11] and the OKF models developed by our group. In the state-space models, Tc is not directly observed but considered as a latent state variable, while the other measurable physiological variables (e.g.…”

Section: Kalman Filter Modelsmentioning

confidence: 99%

“…Our group extended the aforementioned work by proposing an EKF model in which both HR and ST are considered in the time update function and the nonlinear dependence is used in the time update function [11]. Moreover, work-rest regime-switching models were proposed to describe the different Tc dependency on HR and ST during the march (work) and the recess (rest) states.…”

Section: Extended Kalman Filtermentioning

confidence: 99%

“…Similarly, because heat can be conducted from deep tissues to skin, an increase in Tc can lead to an elevation of ST over time [9]. Previous studies have also shown the promise of using HR and ST to estimate heat strain [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…Further, the validity of the Tc estimates in human subjects with differing demographics and working in a predominantly hot and humid climate was unclear. We implemented an extended Kalman filter (EKF) model using a non-linear (cubic) state space model (ST versus Tc) with a stage-wise, autoregressive exogenous model (incorporating HR) as the time update model [11]. We showed that the EKF model predicted Tc more precisely [root mean square error (RMSE) was 0.29 C] compared to KF models that relied only on HR as an explanatory variable (RMSD = 0.33 C).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Kalman Filter Models for the Prediction of Individualised Thermal Work Strain

Guo¹,

Chen²,

Fan³

et al. 2018

Kalman Filters - Theory for Advanced Applications

Self Cite

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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 laboratoryand 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).

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An IR Sensor Based Smart System to Approximate Core Body Temperature

Ray

2017

J Med Syst

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Herein demonstrated experiment studies two methods, namely convection and body resistance, to approximate human core body temperature. The proposed system is highly energy efficient that consumes only 165 mW power and runs on 5 VDC source. The implemented solution employs an IR thermographic sensor of industry grade along with AT Mega 328 breakout board. Ordinarily, the IR sensor is placed 1.5-30 cm away from human forehead (i.e., non-invasive) and measured the raw data in terms of skin and ambient temperature which is then converted using appropriate approximation formula to find out core body temperature. The raw data is plotted, visualized, and stored instantaneously in a local machine by means of two tools such as Makerplot, and JAVA-JAR. The test is performed when human object is in complete rest and after 10 min of walk. Achieved results are compared with the CoreTemp CM-210 sensor (by Terumo, Japan) which is calculated to be 0.7 °F different from the average value of BCT, obtained by the proposed IR sensor system. Upon a slight modification, the presented model can be connected with a remotely placed Internet of Things cloud service, which may be useful to inform and predict the user's core body temperature through a probabilistic view. It is also comprehended that such system can be useful as wearable device to be worn on at the hat attachable way.

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Tracking body core temperature in military thermal environments: An extended Kalman filter approach

Cited by 9 publications

References 9 publications

Accuracy of Algorithm to Non-Invasively Predict Core Body Temperature Using the Kenzen Wearable Device

Accuracy of Algorithm to Non-Invasively Predict Core Body Temperature Using the Kenzen Wearable Device

Kalman Filter Models for the Prediction of Individualised Thermal Work Strain

An IR Sensor Based Smart System to Approximate Core Body Temperature

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