This paper presents the evaluation a 3D shape model of the human head. A statistical shape model of the head is created from a set of 100 MRI scans. The ability of the shape model to predict new head shapes is evaluated by considering the prediction error distributions. The effect of using intuitive anthropometric measurements as parameters is examined and the sensitivity to measurement errors is determined. Using all anthropometric measurements, the average prediction error is 1.60 ± 0.36 mm, which shows the feasibility of the new parameters. The most sensitive measurement is the ear height, the least sensitive is the arc length. Finally, two applications of the anthropometric shape model are considered: the study of the male and female population and the design of a brain-computer interface headset. The results show that an anthropometric shape model can be a valuable tool for both research and design.

In this study, static and dynamic compression and crushing tests were conducted on expanded polystyrene (EPS) foam for material characterisation at high strain rates. This was done to obtain the stress-strain curve for different temperatures and densities. An influence of the strain rate on the experimental data was shown. The resulting curves for modelling were extracted from the experimental data, which were obtained from high speed drop tower tests. The methodology for the processing of the experimental data for use in the finite element (FE) modelling was presented. The foam material model of LS-Dyna was used to simulate the dynamic compression process. This model is dedicated to modelling crushable foam with optional damping, tension cut-off, and strain rate effects. The adjustment of the material parameters for successful modelling has been reported. This FE model of EPS foam was validated with experimental data using impact on a "kerbstone" support. This model can be applied for simulation of dynamic loads on a bicycle helmet. It is useful for designing a reliable bicycle helmet geometry for different types of accidents.

PET scans of the mouse brain are usually performed with anesthesia to immobilize the animal. However, it is desirable to avoid the confounding factor of anesthesia in mouse-brain response. Methods: We developed and validated brain PET imaging of awake, freely moving mice. Head-motion tracking was performed using radioactive point-source markers, and we used the tracking information for PET-image motion correction. Regional 18 F-FDG brain uptake in a test, retest, and memantine-challenge study was measured in awake ( n = 8) and anesthetized ( n = 8) C57BL/6 mice. An awake uptake period was considered for the anesthesia scans. Results: Awake (motion-corrected) PET images showed an 18 F-FDG uptake pattern comparable to the pattern of anesthetized mice. The test–retest variability (represented by the intraclass correlation coefficient) of the regional SUV quantification in the awake animals (0.424–0.555) was marginally lower than that in the anesthetized animals (intraclass correlation coefficient, 0.491–0.629) over the different regions. The increased memantine-induced 18 F-FDG uptake was more pronounced in awake (+63.6%) than in anesthetized (+24.2%) animals. Additional behavioral information, acquired for awake animals, showed increased motor activity on a memantine challenge (total distance traveled, 18.2 ± 5.28 m) compared with test–retest (6.49 ± 2.21 m). Conclusion: The present method enables brain PET imaging on awake mice, thereby avoiding the confounding effects of anesthesia on the PET reading. It allows the simultaneous measurement of behavioral information during PET acquisitions. The method does not require any additional hardware, and it can be deployed in typical high-throughput scan protocols.

Police crash reports are often the main source for official data in many countries. However, with the exception of fatal crashes, crashes are often underreported in a biased manner. Consequently, the countermeasures adopted according to them may be inefficient. In the case of bicycle crashes, this bias is most acute and it probably varies across countries, with some of them being more prone to reporting accidents to police than others. Assessing if this bias occurs and the size of it can be of great importance for evaluating the risks associated with bicycling. This study utilized data collected in the COST TU1101 action "Towards safer bicycling through optimization of bicycle helmets and usage". The data came from an online survey that included questions related to bicyclists' attitudes, behaviour, cycling habits, accidents, and patterns of use of helmets. The survey was filled by 8655 bicyclists from 30 different countries. After applying various exclusion factors, 7015 questionnaires filled by adult cyclists from 17 countries, each with at least 100 valid responses, remained in our sample. The results showed that across all countries, an average of only 10% of all crashes were reported to the police, with a wide range among countries: from a minimum of 0.0% (Israel) and 2.6% (Croatia) to a maximum of a 35.0% (Germany). Some factors associated with the reporting levels were type of crash, type of vehicle involved, and injury severity. No relation was found between the likelihood of reporting and the cyclist's gender, age, educational level, marital status, being a parent, use of helmet, and type of bicycle. The significant under-reporting - including injury crashes that do not lead to hospitalization - justifies the use of self-report survey data for assessment of bicycling crash patterns as they relate to (1) crash risk issues such as location, infrastructure, cyclists' characteristics, and use of helmet and (2) strategic approaches to bicycle crash prevention and injury reduction.

Aside from anthropometric data tables, 3D shape models of the human body are becoming increasingly common and call for new product sizing methods based on 3D anthropometry. Though some shape model-based methods exist, most of them focus on mathematical clustering and do not discuss the usability of the clustering results for product design. In this paper, a new shape-model based clustering method for product sizing is presented that takes into account both shape information and usability for designers. The new method, called constrained k-medoids clustering, is applied on a shape model of 100 human heads. It is compared to a partitioning around medoid (PAM) clustering of anthropometric measurements of the same 100 heads (i.e., feature-based), as well as to PAM clustering of the shape model (i.e., shape based). Results show that both shape-based and constrained clustering perform better than feature-based clustering, with an average size-weighted variance (SWV) of 62 × 10 3 ± 16 × 10 3 and 66 × 10 3 ± 26 × 10 3 as compared to 72 × 10 3 ± 12 × 10 3 , respectively. The average point-to-point distances in shape-based and constrained k-medoids were found to be similar to those of feature-based k-medoids, indicating that using 3D-anthropometry for product sizing will not have a negative impact on designer workload and/or a higher cost to implement more sizes. The results suggest that for head-based products, which require accurate shape and size fit, sizing systems should be created using either shape-based or constrained k-medoids, with the latter being slightly less accurate but more intuitive for further design and verification.

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