aceA supervised machine learning approach to predict core temperature for workers with high-level personal protective equipment
This study assesses machine-learning approaches to predict core temperature (Tc) in workers wearing high-level personal protective equipment (PPE), hypothesizing improved accuracy over conventional methods. Methods Fifty participants (48 male, 2 female) performed exercise protocols while wearing high-level PPE in one of two conditions (NORM [n = 14]: 25-26°C, 45% humidity, HYPR [n = 36]: 35-36°C, 45-50% humidity). The variables collected were skin temperature (Tsk), heart rate (HR), time, respiratory rate (RR),…
A supervised machine learning approach to predict core temperature for workers with high-level personal protective equipment: Results LOSO approach: GAM model (R 2 of 0.84, a bias of 0.00, a 95% LoA [-0.50°C, 0.50°C], an RMSE of 0.26°C, an MAE of 0.21°C, and a SEE of 0.26°C), RF model (R 2 of 0.84, a bias of 0.01, a 95% LoA [-0.50°C, 0.50°C], an RMSE of 0.26°C, an MAE of 0.20°C, and a SEE of 0.26°C). 80/20 approach: GAM model (R 2 of 0.89, a bias of 0.00, a 95% LoA [-0.41°C, 0.41°C], an RMSE of 0.21°C, an MAE of 0.17°C, and a SEE of 0.21°C), RF model (R 2 of 0.96, a bias of 0.00, a 95% LoA [-0.26°C, 0.26°C], an RMSE of 0.13°C, an MAE of 0.09°C, and a SEE of 0.21°C).
Evidence
- Peer-reviewedApplied Ergonomics2026-10-06
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Truvace Impact Record TRV-2026-1808, v1: “A supervised machine learning approach to predict core temperature for workers with high-level personal protective equipment.” Truvace, 2026-10-18. /record/TRV-2026-1808 (accessed at citation time). sha256 d585859d356d9ae0…
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