Featured Application: Improving garment selection without manual measuring for made-to-order sports garments. 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.

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Gaining aerodynamic advantage is crucial in cycling, wherein drag force can account for 90% of resistance to a cyclist. The interaction of wind with the moving human and cycling equipment also contributes to drag. Testing equipment for aerodynamic properties in a wind tunnel is of interest to manufacturers in the sports industry. However, a protocol that needs to depend on the ability of a human to accurately recreate desired poses and movements multiple times may be cumbersome during testing. Additionally, a stationary mannequin does not accurately represent real-life cycling, where pedaling is a constant. Hence, a moving mannequin is suited for such applications. In this contribution, we demonstrate the development of a 3D printed articulating cycling mannequin. A cyclist was scanned using a structured light 3D scanner. Our goal was to develop an articulating mannequin with pedalling legs and a detachable upper-body to recreate two common cycling poses - time-trial and regular. In this contribution, we describe the full development of the mannequin in time trial pose. This approach outlines a low-cost method to developing mannequins for testing applications in sports. @en

In times of online shopping, it is a challenge to select the right size of the desired clothing without fitting it before ordering. Therefore, this study describes three techniques to predict 3D upper body dimensions. The first method used basic personal parameters (gender, age, weight and length), the second technique used also the shoulder width and the last method used a 3D Styku scan to add extra input parameters. The accuracy of the three prediction methods was compared against hand measurements for 17 upper body dimensions of 37 subjects. The Intraclass Correlation Coefficient increases with 11.2% for the Styku method compared to the other methods. For chest, hip and waist measurements, the basic method is reliable to predict 3D body dimensions and indicate the right size from an existing collection. For more accurate upper body dimensions as needed for producing custom made clothing, a 3D Styku scan can supply the desired input.

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Thermal performance of three bicycle helmets for latent heat loss was evaluated through a virtual testing methodology using Computational fluid dynamics (CFD) simulations. The virtual thermal manikin was prescribed with a constant sweat rate of 2 g/h and a constant sweat film thickness of 0.3 mm. The simulations were carried out at 6 m/s until convergence was achieved. The results from steady state simulations show heat loss of 158 W from manikin without helmet and approximately 135 W with helmets. However, the thermal performance of helmets with a sweating manikin has been reduced from 89–93% to 84–87%. These results imply that evaporative/latent heat loss plays a significant role in thermal performance of helmets. Therefore, thermal performance tests for helmets should also include testing of helmets for evaporative heat loss.@en

Thermal performance of three bicycle helmets for radiative and convective heat loss was evaluated through heat loss experiments in a wind tunnel. A 3D printed thermal manikin head of a 50th percentile western male population was developed. Thermal performance of a helmet was quantified by comparing the manikin head heat losses with and without helmet. Experiments were performed for two air velocities: 1.6 m/s and 6 m/s. An infrared heat lamp positioned above the manikin simulated the effect of solar load. The results from the experiments showed a convective cooling efficiency between 89% and 96% for open helmets and between 78% and 83% for closed helmets. The radiative heat gain ranged from 3.5 W to 4.5 W for open helmets and 5 W to 8 W for closed helmets.@en

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@en

Parametric 3D human body models are valuable tools for ergonomic product design and statistical shape modelling (SSM) is a powerful technique to build realistic body models from a database of 3D scans. Like the underlying 3D scans, body models built from SSMs are typically represented with triangle meshes. Unfortunately, triangle meshes are not well supported by CAD software where spline geometry dominates. Therefore, we propose a methodology to convert databases of pre-corresponded triangle meshes into multi-patch B-spline SSMs. An evaluation on four 3D scan databases shows that our method is able to generate accurate and water-tight models while preserving inter-subject correspondences by construction. In addition, we demonstrate that such SSMs can be used to generate design manikins which can be readily used in SolidWorks for designing well conforming product parts.@en

Measuring and interpretation of brain wave signals through electroencephalography (EEG) is an emerging technology. The technique is traditionally applied in a clinical setting with EEG caps and conductive gels to ensure proper contact through a subject’s hair, and anticipate inter-subject anthropometric variations. Development of dry electrodes offers the potential to develop wearable EEG headsets. Such devices could induce medical and commercial applications. In this paper, we evaluate a prototype EEG headset that actively places electrodes at standardized positions on the subject’s head, where each electrode is applied with equal pressure. The system is designed for use with dry electrodes. Our research delivers a better understanding on the link between general level of comfort and possible useful clear data signals, that can be used in brain computer interfaces (BCI). The present study is confined to the impact of adjustable electrodes pressure on level of user comfort only. Levels of discomfort are assessed in twelve participants, wearing an EEG headset with controllable electrode pressure exerted at 14 locations. Of-the-shelf dry electrodes are used. In a first session, evenly distributed pressure is increased and afterwards decreased in fixed time intervals, going from 10 kPa to 30 kPa and vice versa with steps of 2 kPa. In a second session, a subject specific acceptable pressure level is retrieved from the data of the first session and constantly applied for 30 min. During this intervention, level of discomfort is assessed in a VAS-scale. Additional observation and surveys yields insights on user experience in wearing a pressure exerting EEG headset.@en