The main objective of the study was to investigate the thermal performance of five (open and closed) bicycle helmets for convective and evaporative heat transfer using a nine-zone thermal manikin. The shape of the thermal manikin was obtained by averaging the 3D-point coordinates of the head over a sample of 85 head scans of human subjects, obtained through magnetic resonance imaging (MRI) and 3D-printed. Experiments were carried out in two stages, (i) a convective test and (ii) an evaporative test, with ambient temperature maintained at 20.5 ± 0.5 °C and manikin skin temperature at 30.5 ± 0.5 °C for both the tests. Results showed that the evaporative heat transfer contributed up to 51%–53% of the total heat loss from the nude head. For the convective tests, the open helmet A1 having the highest number of vents among tested helmets showed the highest cooling efficiency at 3 m/s (100.9%) and at 6 m/s (101.6%) and the closed helmet (A2) with fewer inlets and outlets and limited internal channels showed the lowest cooling efficiency at 3 m/s (75.6%) and at 6 m/s (84.4%). For the evaporative tests, the open helmet A1 showed the highest cooling efficiency at 3 m/s (97.8%), the open helmet A4 showed the highest cooling efficiency at 6 m/s (96.7%) and the closed helmet A2 showed the lowest cooling efficiency at 3 m/s (79.8%) and at 6 m/s (89.9%). Two-way analysis of variance (ANOVA) showed that the zonal heat-flux values for the two tested velocities were significantly different (p < 0.05) for both the modes of heat transfer. For the convective tests, at 3 m/s, the frontal zone (256–283 W/m²) recorded the highest heat flux for open helmets, the facial zone (210–212 W/m²) recorded the highest heat flux for closed helmets and the parietal zone (54–123 W/m²) recorded the lowest heat flux values for all helmets. At 6 m/s, the frontal zone (233–310 W/m²) recorded the highest heat flux for open helmets and the closed helmet H1, the facial zone (266 W/m²) recorded the highest heat flux for the closed helmet A2 and the parietal zone (65–123 W/m²) recorded the lowest heat flux for all the helmets. For evaporative tests, at 3 m/s, the frontal zone (547–615 W/m²) recorded the highest heat flux for all open helmets and the closed helmet H1, the facial zone (469 W/m²) recorded the highest heat flux for the closed helmet A2 and the parietal zone (61–204 W/m²) recorded the lowest heat flux for all helmets. At 6 m/s, the frontal zone (564–621 W/m²) recorded highest heat flux for all the helmets and the parietal zone (97–260 W/m²) recorded the lowest heat flux for all helmets.

As sports garments are stretchable, different sizing tables are used than for retail clothing. However, customers measuring themselves leads to errors and unsatisfaction, since these customized branded garments cannot be returned. Using fitting sets avoids this, but this is not always feasible, especially in an online retail environment. Therefore, this research aims to use descriptive measures—parameters that do not require manual measuring because they are readily known by heart by almost any customer—to predict users’ body measurements, which can, thus, be used by customers to determine the size of their sports garment from a sizing chart. To validate if these input measures are sufficient to predict the correct size, three prediction methods are used and compared with baseline manual measurements. The methods are: (i) clothing size predictions from shape models with descriptive measures as inputs, (ii) clothing size predictions from a regression analysis, and (iii) clothing size predictions from a shape model based on extensive 3D scanned measurements as input. The conclusion is that a regression algorithm with, as input variables, the straightforward demographics of age, gender, stature, and weight is more accurate than the algorithm with the same inputs but with a shape model behind it. Moreover, chest and hip circumferences have an intraclass correlation coefficient rating above 0.9 and are, thus, suited for online retail of stretchable garments, such as cycling clothes. As validated by end-users, the regression predictions are shown to agree with preferred garment sizes of the participants, within the natural variation of personal preferences.

(1) Background: Many professional and recreational cyclists experience that neck extension in time trial position negatively impacts either speed, comfort, or power production—especially at high cycling speeds or for long distances. We conducted a feasibility study with one subject to assess whether redirecting the sight of a cyclist while in time trial position could reduce aerodynamic drag and neck strain by maintaining a more neutral neck position. (2) Methods: A physical immersive exercise bike was developed (called a FAAST-trainer) that emulates posture, velocity, and power to be delivered by the user through an adaptable power load adjusted in real time. As an optical intervention, we used prism glasses to redirect the cyclist’s sight. The subject trained his perceptive-muscular system while cycling on the FAAST-trainer to get used to wearing prism glasses. He feels confident that the glasses are safe to test for future experiments in a velodrome. (3) Results: A consistent reduction in drag was found (p < 001) when wearing prism glasses with the FAAST-trainer, ranging from 3.5% to 4.7%. Accordingly, the cyclist could thus save between 9.7 watts and 13.0 watts cycling at 45 km/h, compared to having his head in an upright position. (4) Conclusions: Our experiment on the FAAST-trainer indicates that an optical intervention to reduce neck extension by redirecting sight might be safe to use for outdoor cycling. However, no vestibular effects, neither auditive nor complex combinations, were assessed, so we recommend additional research and development of a dedicated design for the prism glasses. Outdoor experiments should be conducted to confirm this reduction in aerodynamic drag and further asses the safety when wearing prism glasses.

Designing for the body requires bridging the gap between physical complexity and practical solutions. This doctoral thesis explores research by prototyping as a pragmatic design research strategy for the development of wearable products. It presents evidence that rapid prototyping can help simulate complex phenomena, may assist in predicting difficult-to-measure parameters, and can surface previously unknown design requirements. The work addresses the multifaceted challenges of wearable product design (fit, comfort, usability, and physiological performance) by integrating empirical validation with digital and physical prototyping workflows. These include computer-aided design (CAD), statistical shape modelling, anthropometry, and ergonomic physiology. The research is structured around four core challenges: from the utility of anthropometric data and shape modelling techniques, to developing products based on these models, to prototyping for specific physiological effects within the body, and culminating in rapid development under societal urgency, exemplified by emergency-use respirators during the COVID-19 pandemic. Contributions include the prediction of body shape using statistical shape models (SSMs), their application in ergonomic product design, and the integration of internal physiological processes into wearable development. Validation is achieved through physical experiments such as scalp shape prediction, EEG headset design, muscle stiffness measurement via a palpation device, and thermal performance evaluation using a biofidelic thermal manikin. The thesis concludes with the industrial production of Poly2+ face masks, illustrating how prototyping transcends its traditional role as a design tool to become a generator of knowledge. Across all chapters, the research emphasizes user-centred design, empirical validation, and interdisciplinary collaboration, offering scalable frameworks for future wearable product development across personalized, conventional sizing systems and mass-customised contexts.