A century of exercise physiology: concepts that ignited the study of human thermoregulation. Part 3: Heat and cold tolerance during exercise

Notley, Sean R.; Mitchell, Duncan; Taylor, Nigel A. S.

doi:10.1007/s00421-023-05276-3

Cited by 5 publications

(3 citation statements)

References 1,520 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the application of the KDM approach to the development of an equivalent temperature index will require the most careful design of the underlying database, which will implicitly also define the (unknown) reference to which all conditions will be compared. Therefore, we believe that the UTCI approach with a specified reference will promote results that are more interpretable in terms of thermal stress and physiological strain [63].…”

Section: Extended Application By Ensemble Modellingmentioning

confidence: 99%

Skills of Statistical Learning Algorithms in Thermal Stress Assessment Compared with the Expert Judgement Inherent to the Universal Thermal Climate Index (UTCI)

Bröde,

Fiala,

Kampmann

2024

Preprint

View full text Add to dashboard Cite

The objective of this paper was to verify the applicability of statistical learning (SL) compared to human reasoning with respect to the Universal Thermal Climate Index (UTCI), a complex tool for the assessment of outdoor thermal stress. UTCI is an equivalent temperature index based on the 48-dimensional output of an advanced model of human thermoregulation formed by 12 variables at four consecutive 30-minute intervals, which were calculated for 105642 thermal conditions from extreme cold to extreme heat. Comparing the performance of SL algorithms to the results accomplished by an international endeavor involving more than 40 experts from 23 countries, we found that random forests and k-nearest neighbors closely predicted UTCI values, but that clustering applied after dimension reduction algorithms (principal component analysis and t-distributed stochastic neighbor embedding) were inadequate for risk assessment in relation to the UTCI stress categories. This indicates a potential supportive role for SL, as it will not (yet) fully replace the bio-meteorological expert knowledge.

show abstract

Section: Extended Application By Ensemble Modellingmentioning

confidence: 99%

Skills of Statistical Learning Algorithms in Thermal Stress Assessment Compared with the Expert Judgement Inherent to the Universal Thermal Climate Index (UTCI)

Bröde,

Fiala,

Kampmann

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…On the other hand, it will make the resulting equi temperature less interpretable due to the unspecified reference conditions. Consequ the utilization of the KDM algorithm for the development of an equivalent tempe index will require the most careful design of the underlying database, whic implicitly also define the (unknown) reference to which all conditions will be comp Hence, we believe that the UTCI approach with a specified reference will pr results that are more interpretable in terms of thermal stress and physiological which forms an essential requirement for assessment procedures concerning the h responses to the thermal environment [12,61].…”

Section: Alternative Approach By Ensemble Modelingmentioning

confidence: 99%

“…Hence, we believe that the UTCI approach with a specified reference will promote results that are more interpretable in terms of thermal stress and physiological strain, which forms an essential requirement for assessment procedures concerning the human responses to the thermal environment [12,61].…”

Section: Limitations and Outlookmentioning

confidence: 99%

Application of Statistical Learning Algorithms in Thermal Stress Assessment in Comparison with the Expert Judgment Inherent to the Universal Thermal Climate Index (UTCI)

Bröde,

Fiala,

Kampmann

2024

Atmosphere

View full text Add to dashboard Cite

This study concerns the application of statistical learning (SL) in thermal stress assessment compared to the results accomplished by an international expert group when developing the Universal Thermal Climate Index (UTCI). The performance of diverse SL algorithms in predicting UTCI equivalent temperatures and in thermal stress assessment was assessed by root mean squared errors (RMSE) and Cohen’s kappa. A total of 48 predictors formed by 12 variables at four consecutive 30 min intervals were obtained as the output of an advanced human thermoregulation model, calculated for 105,642 conditions from extreme cold to extreme heat. Random forests and k-nearest neighbors closely predicted UTCI equivalent temperatures with an RMSE about 3 °C. However, clustering applied after dimension reduction (principal component analysis and t-distributed stochastic neighbor embedding) was inadequate for thermal stress assessment, showing low to fair agreement with the UTCI stress categories (Cohen’s kappa < 0.4). The findings of this study will inform the purposeful application of SL in thermal stress assessment, where they will support the biometeorological expert.

show abstract

A century of exercise physiology: concepts that ignited the study of human thermoregulation. Part 4: evolution, thermal adaptation and unsupported theories of thermoregulation

Notley,

Mitchell,

Taylor

2023

Eur J Appl Physiol

View full text Add to dashboard Cite

A century of exercise physiology: concepts that ignited the study of human thermoregulation. Part 3: Heat and cold tolerance during exercise

Cited by 5 publications

References 1,520 publications

Skills of Statistical Learning Algorithms in Thermal Stress Assessment Compared with the Expert Judgement Inherent to the Universal Thermal Climate Index (UTCI)

Skills of Statistical Learning Algorithms in Thermal Stress Assessment Compared with the Expert Judgement Inherent to the Universal Thermal Climate Index (UTCI)

Application of Statistical Learning Algorithms in Thermal Stress Assessment in Comparison with the Expert Judgment Inherent to the Universal Thermal Climate Index (UTCI)

A century of exercise physiology: concepts that ignited the study of human thermoregulation. Part 4: evolution, thermal adaptation and unsupported theories of thermoregulation

Contact Info

Product

Resources

About