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

Nicole E. Moyen, Rohit C. Bapat, Beverly Tan, Lindsey A. Hunt, Ollie Jay and Toby Mündel
Additional contact information
Nicole E. Moyen: Kenzen, Inc., Kansas City, MO 64108, USA
Rohit C. Bapat: Kenzen, Inc., Kansas City, MO 64108, USA
Beverly Tan: Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
Lindsey A. Hunt: Thermal Ergonomics Laboratory, School of Health and Science, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
Ollie Jay: Thermal Ergonomics Laboratory, School of Health and Science, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
Toby Mündel: School of Sport, Exercise and Nutrition, Massey University, Palmerston North 4472, New Zealand

IJERPH, 2021, vol. 18, issue 24, 1-14

Abstract: 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 (T C ) can help workers avoid reaching unsafe T C . However, continuous T C measurements are currently cost-prohibitive or invasive for daily use. Here, we show that Kenzen’s wearable device can accurately predict T C compared to gold standard T C 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 T C algorithm, which uses subject’s real-time physiological data combined with baseline anthropometric data. We show Kenzen’s T C algorithm meets pre-established accuracy criteria compared to gold standard T C : 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 T C algorithm is accurate for a wide range of T C , 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 T C in real-time, thus offering workers protection from heat injuries and illnesses.

Keywords: heat illness; heat injury; heat stress; heart rate; extended Kalman filter; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/24/13126/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/24/13126/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:24:p:13126-:d:700875

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().