Individualized estimation of human core body temperature using noninvasive measurements

Srinivas Laxminarayan, Vineet Rakesh, Tatsuya Oyama, Josh B. Kazman, Ran Yanovich, Itay Ketko, Yoram Epstein, Shawnda Morrison, Jaques Reifman*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

A rising core body temperature (Tc) during strenuous physical activity is a leading indicator of heat-injury risk. Hence, a system that can estimate Tc in real time and provide early warning of an impending temperature rise may enable proactive interventions to reduce the risk of heat injuries. However, real-time field assessment of Tc requires impractical invasive technologies. To address this problem, we developed a mathematical model that describes the relationships between Tc and noninvasive measurements of an individual’s physical activity, heart rate, and skin temperature, and two environmental variables (ambient temperature and relative humidity). A Kalman filter adapts the model parameters to each individual and provides real-time personalized Tc estimates. Using data from three distinct studies, comprising 166 subjects who performed treadmill and cycle ergometer tasks under different experimental conditions, we assessed model performance via the root mean squared error (RMSE). The individualized model yielded an overall average RMSE of 0.33 (SD 0.18)°C, allowing us to reach the same conclusions in each study as those obtained using the Tc measurements. Furthermore, for 22 unique subjects whose Tc exceeded 38.5°C, a potential lower Tc limit of clinical relevance, the average RMSE decreased to 0.25 (SD 0.20)°C. Importantly, these results remained robust in the presence of simulated real-world operational conditions, yielding no more than 16% worse RMSEs when measurements were missing (40%) or laden with added noise. Hence, the individualized model provides a practical means to develop an early warning system for reducing heat-injury risk. NEW & NOTEWORTHY A model that uses an individual’s noninvasive measurements and environmental variables can continually “learn” the individual’s heat-stress response by automatically adapting the model parameters on the fly to provide real-time individualized core body temperature estimates. This individualized model can replace impractical invasive sensors, serving as a practical and effective surrogate for core temperature monitoring.

Original languageEnglish
Pages (from-to)1387-1402
Number of pages16
JournalJournal of Applied Physiology
Volume124
Issue number6
DOIs
StatePublished - Jun 2018

Keywords

  • Core body temperature
  • Heat injury
  • Individualized mathematical model
  • Kalman filter
  • Noninvasive measurements

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