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.
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.
Abstract.The human body is a complex biomechanical system that exhibits many variations. Wearable products should be both functional and comfortable. They require a close and accurate fit to the body of the end-user. Current approaches to design body near products rely on 1D anthropometry and unrealistic manikins, e.g. constructed from simple surfaces such as spheres and cylinders connected by splines. With the uprising of 3D scanning, a myriad of accurate 3D body models becomes available. In this paper we present a framework to use this 3D shape information in the development of wearable products. The key concept that we introduce to achieve this extension, is an enriched shape model: a statistical shape model of the human body that also contains all 1D anthropometric data in it. With enriched shape models, a 3D shape can be parameterized with a given set of anthropometric features. Thus the dense geometric information of an individual's shape can be obtained simply by tuning that individual's anthropometric values. By designing on the generated 3D surface, a product can be obtained that closely fits the individual's shape. We thus extend the method of linking 1D anthropometric data with the dimensions of a product. This results in three design strategies that link both body shape with product geometry: design for collective fit, design for fit within clusters and design for individual fit. Each strategy is explained and studied with the design of wearable EEG headsets that fits the human head. KeywordsMass-customization, EEG headsets, CAD, parameterized design, 3D anthropometry, statistical shape models Relevance to Design PracticeWe present a workflow to use accurate 3D shape models of the human body in the design of products that should closely fit the end-user. To that end, we introduce enriched shape models: a new data structure that contains all dense geometric shape information together with classical anthropometric data. We illustrate how enriched shape models can be used to achieve products with personalized fit, as an extension to the use of univariate anthropometric data. The use of enriched shape models for personalized design could become an important driver for mass customization. To that end, tools and techniques should be developed to incorporate the presented workflow in CAD/CAM.
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.
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