For product developers that design near-body products, virtual mannequins that represent realistic body shapes, are valuable tools. With statistical shape modelling, the variability of such body shapes can be described. Shape variation captured by statistical shape models (SSMs) is often polluted by posture variations, leading to less compact models. In this paper, we propose a framework that has low computational complexity to build a posture invariant SSM, by capturing and correcting the posture of an instance. The posture-normalised SSM is shown to be substantially more compact than the non-posture-normalised SSM. Practitioner summary: Statistical shape modelling is a technique to map out the variability of (body) shapes. This variability is often polluted by variations in posture. In this paper, we propose a framework to build a posture invariant statistical shape model. Abbreviations: SSM: statistical shape model; 1D: one-dimensional; 3D: three-dimensional; DHM: digital human model; LBS: linear blend skinning; PCA: princial component analysis; PC: principal component; TTR: thumb tip reach.@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

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.@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.@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

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