A 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), 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]). Model performance was evaluated using both leave-one-subject-out (LOSO) cross-validation and an observation-level 80/20 split (80/20). The two models' performance was measured using adjusted R2, bias, limits of agreement (LoA), root mean squared error (RMSE), mean absolute error (MAE), and standard error of the estimate (SEE).

Results

LOSO approach: GAM model (R2 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 (R2 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 (R2 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 (R2 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).

Conclusion

Supervised machine learning approaches can effectively predict Tc among workers wearing high-level PPE.