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TRUVACE RECORD VERSION
record: TRV-2026-1808
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-18T10:41:48.170828Z
status: published
lens: p_space
sector: business
headline: A supervised machine learning approach to predict core temperature for workers with high-level personal protective equipment
dek: 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),…
gain_title: (none)
problem_title: 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).
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: 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).
problem_evidence: (none)
quick_read: 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).

A total of 4063 data points were utilized in two separate, complementary supervised machine learning analyses (random forest [RF] and generalized additive models [GAM]). 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).
limitation: 
tag: Evidence-backed problem
key_points: 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), and rate of skin temperature acquisition per minute (Tsk/min).
rundown: 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), and rate of skin temperature acquisition per minute (Tsk/min). A total of 4063 data points were utilized in two separate, complementary supervised machine learning analyses (random forest [RF] and generalized additive models [GAM]).
sources:
- peer_reviewed | Applied Ergonomics | https://doi.org/10.1016/j.apergo.2026.104902 | 2026-10-06
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