Core Temperature Estimation Using Wearable Earbud-Type Thermometer and Machine Learning During Light-Intensity Cycling Under Varying Indoor Ambient Temperatures.

Continuous monitoring of core temperature is crucial for optimizing exercise performance, promoting health, and ensuring safety. Here, we evaluated the validity of a machine learning-based earbud-type core temperature sensor during exercise. At ambient temperatures of 10°C, 20°C, and 30°C, participants rested and then cycled for 25 min (90 W for males and 60 W for females), with an artificial wind of ~3.0 m/s directed at the face during the final 10 min of exercise. Throughout the experiment, two thermistors embedded in the earbud-type device monitored both ambient temperature and internal ear temperature. Additionally, an infrared temperature sensor integrated into the earbud-type thermistor measured internal ear temperature. Core temperature was measured through a machine learning-based approach, and esophageal temperature served as the reference. We created mixed-effects Bland-Altman plots for the relationship between estimated and esophageal temperature, and found that the bias was -0.003°C with a limit of agreement of -0.52°C-0.51°C. Root mean squared error and Pearson's r for the two-temperature relationship were 0.26°C and 0.81, and 76.9% of the data exhibited temperature differences within a margin of ≤ 0.3°C between the two measurements. Similar results were also observed with artificial wind. Including infrared temperature measurements did not enhance the aforementioned variables. We show that a machine learning-based wearable earbud-type thermometer can validly estimate core temperature in exercising individuals across 10°C-30°C indoor ambient temperatures with and without exposure to a wind speed of ~3.0 m/s. However, further improvement of the algorithm is needed to enhance estimation accuracy.