Head sweat rate prediction for thermal comfort assessment of bicycle helmets

Peter Bröde,Dusan Fiala,Tiago S Mayor,Jean-Marie Aerts,Guido De Bruyne

doi:10.1186/2046-7648-4-s1-a85

Abstract

Only 1% to 40% of adult cyclists in European countries make use of bicycle helmets. This may be partly attributable to impaired thermal comfort associated with helmet use, which can lead to locally accumulated sweat and increased skin wettedness [1]. Local sweat rates (LSR) can be modelled by sudomotor sensitivities (SUD) relating the change in LSR to the change in body core temperature (TC) [2], or by the ratio of LSR to gross sweat rate (GSR) of the whole body [3]. Coupling those local models with models of thermoregulation predicting TC and GSR provides a framework for predicting head sweat rates in response to the characteristics of the thermal environment, clothing, level of activity and exposure time. This paper studies the influence of different local and whole-body models on predictive accuracy.

Highlights

1% to 40% of adult cyclists in European countries make use of bicycle helmets
This may be partly attributable to impaired thermal comfort associated with helmet use, which can lead to locally accumulated sweat and increased skin wettedness [1]
We identified six different local models for the head region relying on SUD [2,3,4] and three models using Local sweat rates (LSR) to gross sweat rate (GSR) ratios [3]

Summary

Introduction

1% to 40% of adult cyclists in European countries make use of bicycle helmets. This may be partly attributable to impaired thermal comfort associated with helmet use, which can lead to locally accumulated sweat and increased skin wettedness [1]. Local sweat rates (LSR) can be modelled by sudomotor sensitivities (SUD) relating the change in LSR to the change in body core temperature (TC) [2], or by the ratio of LSR to gross sweat rate (GSR) of the whole body [3]. Coupling those local models with models of thermoregulation predicting TC and GSR provides a framework for predicting head sweat rates in response to the characteristics of the thermal environment, clothing, level of activity and exposure time. This paper studies the influence of different local and whole-body models on predictive accuracy

Methods

Conclusion

ISO 7933