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Open AccessArticle

Towards Model-Based Online Monitoring of Cyclist’s Head Thermal Comfort: Smart Helmet Concept and Prototype

Department of Biosystems, Animal and Human Health Engineering Division, M3-BIORES: Measure, Model & Manage of Bioresponses Laboratory, KU Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium
FAME Laboratory, Department of Exercise Science, University of Thessaly, 410-00 Thessaly, Greece
SIMTECH Laboratory, Transport Phenomena Research Centre, Engineering Faculty of Porto University, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
Department of Product Development, University of Antwerp, 2018 Antwerp, Belgium
Lazer Sport NV, Lamorinierestraat 33-37, 2018 Antwerp, Belgium
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(15), 3170;
Received: 10 July 2019 / Revised: 29 July 2019 / Accepted: 1 August 2019 / Published: 4 August 2019
(This article belongs to the Special Issue Human Health Engineering)
Bicyclists can be subjected to crashes, which can cause injuries over the whole body, especially the head. Head injuries can be prevented by wearing bicycle helmets; however, bicycle helmets are frequently not worn due to a variety of reasons. One of the most common complaints about wearing bicycle helmets relates to thermal discomfort. So far, insufficient attention has been given to the thermal performance of helmets. This paper aimed to introduce and develop an adaptive model for the online monitoring of head thermal comfort based on easily measured variables, which can be measured continuously using impeded sensors in the helmet. During the course of this work, 22 participants in total were subjected to different levels of environmental conditions (air temperature, air velocity, mechanical work and helmet thermal resistance) to develop a general model to predict head thermal comfort. A reduced-order general linear regression model with three input variables, namely, temperature difference between ambient temperature and average under-helmet temperature, cyclist’s heart rate and the interaction between ambient temperature and helmet thermal resistance, was the most suitable to predict the cyclist’s head thermal comfort and showed maximum mean absolute percentage error (MAPE) of 8.4%. Based on the selected model variables, a smart helmet prototype (SmartHelmet) was developed using impeded sensing technology, which was used to validate the developed general model. Finally, we introduced a framework of calculation for an adaptive personalised model to predict head thermal comfort based on streaming data from the SmartHelmet prototype. View Full-Text
Keywords: thermal comfort; bicycle helmet; smart wearables; adaptive model; streaming data thermal comfort; bicycle helmet; smart wearables; adaptive model; streaming data
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Youssef, A.; Colon, J.; Mantzios, K.; Gkiata, P.; Mayor, T.S.; Flouris, A.D.; De Bruyne, G.; Aerts, J.-M. Towards Model-Based Online Monitoring of Cyclist’s Head Thermal Comfort: Smart Helmet Concept and Prototype. Appl. Sci. 2019, 9, 3170.

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