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 (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.
Highlights
As climate change is increasing average temperatures globally, and the frequency of extreme heat events [1], an increasing number of workers will be exposed to these hotter temperatures on a more frequent basis [2,3,4,5,6,7]
In the Extended Kalman Filter (EKF) model, the initial seed value can be set at a standard TC value (e.g., 37 ◦ C), or it can be set based on the learning for that specific person
Through various iterations of seed values, and evaluating the model performance, we found that the seed values that led to the most accurate EKF models were based on a separate linear regression model where biological sex was the sole input
Summary
As climate change is increasing average temperatures globally, and the frequency of extreme heat events [1], an increasing number of workers (e.g., military, construction, agriculture, etc.) will be exposed to these hotter temperatures on a more frequent basis [2,3,4,5,6,7]. Workers experience a 2% loss in productivity for each 1 ◦ C increase in wet bulb globe temperature (WBGT) ≥ 24 ◦ C [6], and in addition to productivity losses, the number of heat-related injuries and illnesses at job sites is on the rise. In construction settings, the risk of heat-related deaths has increased since the 1990s, and is predicted to continue to increase unless heat-mitigation strategies are adopted at these sites [7]. One such heat-mitigation strategy is to monitor core temperature (TC ) and alert workers when they reach temperature thresholds that predispose workers to heat-related injuries and illnesses (i.e., 38.2–38.5 ◦ C; [8]). Accurately monitoring individuals’ TC on a daily basis at
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