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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),…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-1808· cite & verify
A supervised machine learning approach to predict core temperature for workers with high-level personal protective equipment
Reactive shear layer mixing and growth rate effects on afterburning properties for axisymetric rocket engine plumes by Hartsfield, Carl Rex.. Public domain

In brief

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).

Main points

  1. 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.
  2. 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).
  3. The variables collected were skin temperature (Tsk), heart rate (HR), time, respiratory rate (RR), and rate of skin temperature acquisition per minute (Tsk/min).

The problem

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).

The rundown

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

  1. Peer-reviewedApplied Ergonomics2026-10-06

The debate