Bumping Elbows explores a workflow integrating 3D body scanning technology with robotic knitting to create personalized garments. Traditional 3D knitting development relies on 2D drafts and panels, rooted in industrial flatbed knitting practices. Our approach leverages accurate topology measurements from 3D body scans to directly inform garment design and production, allowing for custom fits to unique body shapes. We will demonstrate this process through live 3D scanning and software demonstrations, highlighting the challenges and opportunities integrating body scans and knitting techniques like goring. Our included software addresses limitations of previous work and outlines advancements needed for broader research adoption, emphasizing the potential of combining 3D scanning with robotic knitting. This method offers enhanced personalization and sustainability in garment production, showcasing the ongoing challenges and advancements in achieving precision in robotic knitting.
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From the article: Abstract Adjustment and testing of a combination of stochastic and nonstochastic observations is applied to the deformation analysis of a time series of 3D coordinates. Nonstochastic observations are constant values that are treated as if they were observations. They are used to formulate constraints on the unknown parameters of the adjustment problem. Thus they describe deformation patterns. If deformation is absent, the epochs of the time series are supposed to be related via affine, similarity or congruence transformations. S-basis invariant testing of deformation patterns is treated. The model is experimentally validated by showing the procedure for a point set of 3D coordinates, determined from total station measurements during five epochs. The modelling of two patterns, the movement of just one point in several epochs, and of several points, is shown. Full, rank deficient covariance matrices of the 3D coordinates, resulting from free network adjustments of the total station measurements of each epoch, are used in the analysis.
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The short-term aim of this R&D project (financed by the Centre of Expertise Creative Industries) is to develop a virtually simulated textile database that renders 3D visual representations of these fabrics. The idea is for this database to be open source and be able to interface with 3D design applications such as those of Lectra. The textile database will include a number of different digital datasets per textile that contain information about the fabric’s drape, weight, flexibility etc., to virtually render prototypes in a 3D simulated environment. As such, in building garments via a 3D software design application, designers will be able to see how a garment changes as new textiles are applied, and how textiles behave when constructed as different garments. This will take place on 3D avatars, which may be bespoke body scans, and will allow for coordinated and precise fitting and grading.
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The additive manufacturing (AM) of high-quality products requires knowledge of the 3D-printing process and the related design guidelines. Allthough AM has been around for some years, many engineers still lack this knowledge. Therefore, Fontys University of Applied Sciences sets great store by training of engineers, education of engineering students and knowledge sharing on this topic. As an appetiser, this article offers a beginner’s course.
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Abstract Background Visuospatial neglect (VSN) is a cognitive disorder after stroke in which patients fail to consciously process and interact with contralesional stimuli. Visual Scanning Training (VST) is the recommended treatment in clinical guidelines. At the moment, several mixed reality versions of Visual Scanning Training (VST) are being developed. The aim of this study was to explore the opinions of end-users (i.e., therapists) on the use of Virtual Reality (VR) and Augmented Reality (AR) in VSN treatment. Methods Therapists played one VR and two AR Serious Games, and subsequently flled out a questionnaire on User Experience, Usability, and Implementation. Results Sixteen therapists (psychologists, occupational, speech, and physiotherapists) played the games, thirteen of them evaluated the games. Therapists saw great potential in all three games, yet there was room for improvement on the level of usability, especially for tailoring the games to the patient’s needs. Therapists’ opinions were comparable between VR and AR Serious Games. For implementation, therapists stressed the urgency of clear guidelines and instructions. Discussion Even though VR/AR technology is promising for VSN treatment, there is no one-size-fts-all applicability. It may thus be crucial to move towards a plethora of training environments rather than a single standardized mixed reality neglect treatment. Conclusion As therapists see the potential value of mixed reality, it remains important to investigate the efcacy of AR and VR training tools.
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Three-dimensional (3D) body scanning becomes increasingly important in the medical, ergonomical and apparel industry. The SizeStream 3D body scanner is a 3D body scanner in the shape of a fitting room that can generate a 3D copy of the human body in a few seconds. The Poikos modeling system generates a 3D image of a person using a front- and side photo. This study evaluates the repeatability and validity of both systems with human subjects. Hundred fifty-six participants were included in this study, of whom 85 were scanned twice by the SizeStream Scanner and 139 by the Poikos modeling system. The repeatability is assessed by calculating the intra-class correlation coefficients (ICC) and standard error of measurement (SEM), and the validity of 6 Sizestream and 4 Poikos measurements is evaluated by comparing these measurements with collected tape measurements. The ICC and the SEM results indicate that 79 of the 163 SizeStream measurements are repeatable enough to use for fashion purposes, since they had an ICC above 0.80 and a SEM below 10mm. Fifty-one measurements give a good indication but are not accurate enough for pattern making. The waist, chest and hip circumferences are valid after a correction of the over- or underestimation of the measurements. The Poikos modeling system is a promising, but is as expected, less repeatable and valid than the SizeStream scanner. Although the Poikos modeling system can give a good estimation of the body shape, the measurements are not accurate enough (SEM > 10mm) to use in the fashion industry. Future studies have to be performed to validate more Poikos and SizeStream measurements and to assess the usability of these measurements for the fashion industry.
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Technological development offers new opportunities that could changedesign processes. The present study explores the possibilities of technologies likevirtual reality and 3D scan in the furniture design process. For this purpose, a cocreation process with help of new technologies was carried out from initial ideationto 3D modelling. Each tool has been characterized in terms of user experiencemeasured by questionnaire. This research validates a design process of furniturebased on immersive technology and provide some recommendations for theimplementation and improvement of this process.
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To reduce greenhouse gas emissions from the transport sector, shifting to rail transport is crucial. This transition will increase the demand on existing rail infrastructure, necessitating large-scale monitoring to maintain its resilience. Point cloud data are an ideal candidate for this purpose, as they provide immediate, precise 3D geometric information independent of illumination conditions. This study investigates two object detection models, the PointPillar and the CenterPoint model, to automatically create a digital representation of the rail environment. Using a custom open dataset, these two models are evaluated to detect masts, tension rods, signals, and relay cabinets. A mean Average Precision (mAP@0.5) of 70.6% is achieved. A unique contribution of this study is an in-depth analysis of the locational error in terms of the x and y components of the detected positions. This analysis reveals that location accuracy is not yet sufficient for engineering applications. The analysis indicates that the largest contribution to this error originates from the random error. Additionally, this study demonstrates that transfer learning effectively reduces the labeling burden. For instance, when using 25% of the training data, the average Precision (AP) for the tension rod class improves from 9.5% without transfer learning to 70.8% with transfer learning.
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