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Article

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

1
Kenzen, Inc., Kansas City, MO 64108, USA
2
Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
3
School of Sport, Exercise and Nutrition, Massey University, Palmerston North 4472, New Zealand
4
Thermal Ergonomics Laboratory, School of Health and Science, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Emiliano Schena
Int. J. Environ. Res. Public Health 2021, 18(24), 13126; https://doi.org/10.3390/ijerph182413126
Received: 19 October 2021 / Revised: 30 November 2021 / Accepted: 9 December 2021 / Published: 13 December 2021
(This article belongs to the Special Issue Exercise and Human Temperature Regulation)
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. View Full-Text
Keywords: heat illness; heat injury; heat stress; heart rate; extended Kalman filter; machine learning heat illness; heat injury; heat stress; heart rate; extended Kalman filter; machine learning
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MDPI and ACS Style

Moyen, N.E.; Bapat, R.C.; Tan, B.; Hunt, L.A.; Jay, O.; Mündel, T. Accuracy of Algorithm to Non-Invasively Predict Core Body Temperature Using the Kenzen Wearable Device. Int. J. Environ. Res. Public Health 2021, 18, 13126. https://doi.org/10.3390/ijerph182413126

AMA Style

Moyen NE, Bapat RC, Tan B, Hunt LA, Jay O, Mündel T. Accuracy of Algorithm to Non-Invasively Predict Core Body Temperature Using the Kenzen Wearable Device. International Journal of Environmental Research and Public Health. 2021; 18(24):13126. https://doi.org/10.3390/ijerph182413126

Chicago/Turabian Style

Moyen, Nicole E., Rohit C. Bapat, Beverly Tan, Lindsey A. Hunt, Ollie Jay, and Toby Mündel. 2021. "Accuracy of Algorithm to Non-Invasively Predict Core Body Temperature Using the Kenzen Wearable Device" International Journal of Environmental Research and Public Health 18, no. 24: 13126. https://doi.org/10.3390/ijerph182413126

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