Subjective scales are frequently used in the design process of head-related products to assess pressure discomfort. Nevertheless, some users lack fundamental cognitive and motor abilities (e.g., paralyzed patients). Therefore, it is vital to find non-verbal measurements of pressure discomfort and pressure pain. This study gathered the autonomic response data (heart rate and skin conductance) of 30 landmarks in head, neck and face from 31 participants experiencing pressure discomfort and pressure pain. The results indicate that pressure stimulation can change heart rate (HR) and skin conductance (SC). SC can be more useful in assessing pressure discomfort than HR for specific landmarks, and SC also possesses a faster arousal rate than HR. Moreover, HR decreased in response to pressure stimulation, while SC decreased followed by an increase. In comparisons between genders, the subjective pressure discomfort threshold (PDT) and pressure pain threshold (PPT) of women were lower than those of men, but men's autonomic responses (HR and SC) were more intense. Furthermore, there was no linear correlation between subjective pressure thresholds (PDT and PPT) and autonomic response intensity. This study has significant implications for resolving ergonomic issues (pressure discomfort and pain) associated with head-related products.@en

Research in cycling aerodynamics is performed using mannequins of different geometries, which are usually not shared, thus hampering the advancement of our understanding of the flow around a rider on the bike. The primary outcome of this work is to introduce and openly share two anthropometrically realistic generic cyclist models, one in time-trial and one in sprint position. These two models are obtained by averaging the scans of 14 male elite cyclists. The average cyclist geometries are published and openly accessible, making them unique in the field of cycling aerodynamic research. The second objective of this work is to better understand how the difference between the sprint and time-trial position affects the velocity and vortex topology in the wake of a cyclist and, in turn, the aerodynamic drag. Robotic volumetric particle image velocimetry measures the time-average velocity for each mannequin within a wind tunnel. One meter downstream of the lower back, the wakes of the two mannequins are dominated by strong hip/thigh streamwise counter-rotating vortices, which induce a downwash behind the riders’ backs. The strength of these vortices downstream of the sprint model is significantly larger than that of the vortices of the mannequin in the time-trial position. The same holds for a secondary vortex pair that originates from the upper arms and hips. In addition to the vortex strength, the aerodynamic drag area of the sprint model exceeds that of the time-trial model. Hence, it is presumed that stronger vortices relate to higher aerodynamic drag. In contrast to the drag area, the drag coefficient of the two models is the same. Further research is necessary to understand the relation between the cyclist position, the flow topology and the drag coefficient. Finally, the flow around the time-trial model is described in further detail to understand the origin of the different vortex structures. Through comparison to the literature, a vortex topology classification is postulated for the mid-wake and upper-wake. The arm spacing and shoulder width play a critical role in the development of this vortex system.

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Preserving features or local shape characteristics of a mesh using conventional non-rigid registration methods is always difficult, as the preservation and deformation are competing with each other. The challenge is to find a balance between these two terms in the process of the registration, especially in presence of artefacts in the mesh. We present a non-rigid Iterative Closest Points (ICP) algorithm which addresses the challenge as a control problem. An adaptive feedback control scheme with global asymptotic stability is derived to control the stiffness ratio for maximum feature preservation and minimum mesh quality loss during the registration process. A cost function is formulated with the distance term and the stiffness term where the initial stiffness ratio value is defined by an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based predictor regarding the source mesh and the target mesh topology, and the distance between the correspondences. During the registration process, the stiffness ratio of each vertex is continuously adjusted by the intrinsic information, represented by shape descriptors, of the surrounding surface as well as the steps in the registration process. Besides, the estimated process-dependent stiffness ratios are used as dynamic weights for establishing the correspondences in each step of the registration. Experiments on simple geometric shapes as well as 3D scanning datasets indicated that the proposed approach outperforms current methodologies, especially for the regions where features are not eminent and/or there exist interferences between/among features, due to its ability to embed the inherent properties of the surface in the process of the mesh registration.

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4D-scans of dynamic deformable human body parts help researchers have a better understanding of spatiotemporal features. However, reconstructing 4D-scans utilizing multiple asynchronous cameras encounters two main challenges: 1) finding dynamic correspondences among different frames captured by each camera at the timestamps of the camera in terms of dynamic feature recognition, and 2) reconstructing 3D-shapes from the combined point clouds captured by different cameras at asynchronous timestamps in terms of multi-view fusion. Here, we introduce a generic framework able to 1) find and align dynamic features in the 3D-scans captured by each camera using the nonrigid-iterative-closest-farthestpoints algorithm; 2) synchronize scans captured by asynchronous cameras through a novel ADGC-LSTMbased-network capable of aligning 3D-scans captured by different cameras to the timeline of a specific camera; and 3) register a high-quality template to synchronized scans at each timestamp to form a highquality 3D-mesh model using a non-rigid registration method. With a newly developed 4D-foot-scanner, we validate the framework and create the first open-access data-set, namely the 4D-feet. It includes 4Dshapes (15 fps) of the right and left feet of 58 participants (116 feet including 5147 3D-frames), covering significant phases of the gait cycle. The results demonstrate the effectiveness of the proposed framework, especially in synchronizing asynchronous 4D-scans.

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This interactive textbook provides an educational resource into computational design for (industrial) designers. The book focusses on the use of computational design of products/artifacts at a human scale, which might be contrasted by the architectural/build environment scale – a domain which also extensively utilizes computational design principles and tools. Throughout the book, we make use of (commercial) computer-aided-design software, namely Rhinoceros®, and specifically the (build-in) module Grasshopper®. The lessons and knowledge base offered in this book focus on topics that are specifically relevant for and/or attuned to product design (scale), which are categorized in relation to its goal (e.g. design for personalized fit/comfort/aesthetics), by its means (e.g. design for digital fabrication), or for its role in the design process (e.g. for design exploration or design simulation). The book is intended for students both at bachelor and master level. As we believe in a learning-by-doing approach, we aimed for a hands-on, easy-to-get-started set of introductory lessons, which is complemented with a knowledge base. The introductory lessons do not assume any specific prior skills or knowledge (in general or with Rhino Grasshopper) to get started. Yet, (some) experience with computer-aided design (CAD), programming, data processing, and/or mathematics will likely be helpful to really delve into the more complex topics, such as those covered in the knowledge base. The book is currently used as course material in two courses taught at Industrial Design Engineering: “Prototyping with/for Digital Fabrication” (BSc level, part of the Minor Advanced Prototyping), and “Computational design for Digital Fabrication” (MSc level, Elective). The content in this book is in part based on course materials from the above-mentioned courses, which have been been taught to and applied by students with diverse (technical) backgrounds (e.g. industrial design, mechanical engineering, computer science, and electrical engineering). Other parts of the book are inspired by student (graduation) projects and/or follow from research activities by the various contributing authors.@en

When designing wearables that interface with the human head, face and neck, designers and engineers consider human senses, ergonomics and comfort. A dense 3D pressure discomfort threshold map could be helpful, but does not exist yet. Differences in pressure discomfort threshold for areas of the head, neck and face were recorded, to create a 3D pressure discomfort threshold map.

Between 126 and 146 landmarks were placed on the left side of the head, face and neck of twenty-eight healthy participants (gender balanced). The positions of the landmarks were specified using an EEG 10–20 system-based landmark-grid on the head and a self-developed grid on the face and neck. A 3D scan was made to capture the head geometry and landmark coordinates. In a randomised order, pressure was applied on each landmark with a force gauge until the participant indicated experiencing discomfort. By interpolating all collected pressure discomfort thresholds based on their corresponding 3D coordinates, a dense 3D pressure discomfort threshold map was made.

A relatively low-pressure discomfort threshold was found in areas around the nose, neck front, mouth, chin-jaw, cheek and cheekbone, possibly due to the proximate or direct location of nerves, blood veins and soft (muscular) tissue. Medium pressure discomfort was found in the neck back, forehead and temple regions. High pressure discomfort threshold was found in the back of the head and scalp, where skin is relatively thin and closely supported by bone, making these regions interesting for mounting or resting head, face and neck related equipment upon.@en

Human computer models represent a useful tool for investigating the human body response to external static/dynamic loads or for human-centred design. Articulated Total Body (ATB) models are the simplest human multibody models, where body segments are represented by ellipsoids joined at skeletal articulations. Over the years, regression models on both living subjects’ and cadavers’ data have been developed to predict body segments properties. These models are affected by two main limitations: the only inputs are the subject’s weight and height, not considering that for the same combination different morphologies can exist; secondly, regression analyses were performed over a specific population not including peculiar morphologies (under-weight or obese). A novel methodology for developing anthropomorphic ATB models is here presented: a statistical shape model able to predict the external geometry of the human body from a limited set of anthropometric measurements was implemented and body segments were obtained by segmentation; the respective inertial properties were computed from volumes, assuming a constant density value. The properties of this new anthropomorphic ATB model were compared to those calculated by GEBOD (Generator of Body Data), a well-known programme for ATB data calculation. A virtual population of twenty subjects was analysed: with reference to the inertial properties the most relevant differences occurred at the abdomen and the thighs segments (60% relative error), while the trunk, the shoulder and the calves represent the most critical areas for the geometry reconstruction (50 mm average error). The significance of these outcomes was investigated performing multibody simulations with various scenarios.@en

Pressure sensitivity research on the head, face, and neck is critical to develop ways to reduce discomfort caused by pressure in head-related products. The aim of this paper is to provide information for designers to be able to reduce the pressure discomfort by studying the relation between pressure sensitivity and soft tissue in the head, face and neck. We collected pressure discomfort threshold (PDT) and pressure pain threshold (PPT) from 119 landmarks (unilateral) for 36 Chinese subjects. Moreover, soft tissue thickness data on the head, face and neck regions of 50 Chinese people was obtained through CT scanning while tissue deformation data under the PDT and PPT states was obtained from literature. The results of the three-elements correlation analysis revealed that soft tissue thickness is positively correlated with deformation but not an important factor in pressure sensitivity. Our high-precision pressure sensitivity maps confirm earlier findings of more rough pressure sensitivity studies, while also revealing additional fine scale sensitivity differences. Finally, based on the findings, a high-precision "recommended map” of the optimal stress-bearing area of the head, face and neck was generated.

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In this work a new approach for the creation of Articulated Total Body (ATB) models for person-specific multi-body simulations is presented, with the main aim of overcoming limitations related to classical multi-ellipsoids ATB models, based on regression equations having only the weight and the height of the subject as input. The new methodology is based on a Statistical Shape Model (SSM), morphable according to up to 24 input parameters: the SSM was obtained from Principal Component Analysis (PCA), applied on a wide database of 3D human scans (CAESAR). The so obtained geometry can be segmented automatically to generate body segments with the respective inertial properties (mass, principal moments of inertia, and centres of mass location). The routine has been tested on a random set of 20 male subjects and the classical multi-ellipsoids models were compared to these in terms of inertial properties and 3D external geometry: the highest differences were registered at the abdomen and the thighs for what concerns the mass (60%), principal moments (75%) and centres of mass (50 mm) properties; the trunk, the shoulder and the calves are the most critical areas for the external geometry (average distance between the anthropomorphic and ellipsoids models equal to 50 mm). A contribution has been made to build person-specific multibody models. This is a valuable method since approximations made by multi-ellipsoidal models have resulted to be relevant at specific body areas, and personalised models can be a support to design and to forensic analyses.

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Optical motion capturing explains the three-Dimensional (3D) position estimation of points through triangulation employing several depth cameras. Prosperous performance relies on level of visibility of points from different cameras and the overlap of captured meshes in-between. Generally, the accuracy of the estimation is practically based on the camera parameters e.g., location and orientations. Accordingly, the camera network configurations play a key role in the quality of the estimated mesh. This paper proposes an optimal approach for camera placement based on characteristics of a depth camera D435i - Intel RealSense. The optimal problem includes a cost function that contains several minimisation and maximisation terms. The minimisation terms are distance of the cameras to the center of the scanning object, resolution error, and sparsity. And the maximisation terms are distance between each two pair of cameras, percent of captured point from an object, and the level of overlap between cameras. The object is designed based on practical experiments of human walking and is a bounding box around one step of dynamic foot work-space from heel strike posture to toe-off posture. The accuracy and robustness of the algorithms are assessed via experiment measurement, and sensitivity to the number of cameras is investigated. Accordingly, the experiment results determined that the scanning accuracy can be as high as 2.5 % based on a reference scan with a high-end scanner (Artec Eva).

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Fitting apparel and apparel in performing different activities is essential for the functional yet comfortable experience of the user. 4D scans, i.e. 3D scans in continuous timestamps, of the body (part) in performing those activities are the basis for the design of garments/apparel in 4D. In this paper, we proposed a semi-automatic workflow for constructing 4D scans of the body parts with the emphasis on registering noisy scans at a given timestamp. Continuous 3D scans regarding the moving body parts are captured first from different depth cameras from different view angles. In a given timestamp, the collected 3D scans are roughly aligned to a template using the rigid Iterative Closest Points (ICP) algorithm. Then these scans are further registered using a newly proposed non-rigid Iterative Closest-Farthest Points (ICFP) algorithm, in which correspondences between the source and the target are established by either closest or farthest points based on the newly defined logical distance concept and the probability theory. Experimental results indicated that the ICFP method is robust against noise and the scanning accuracy can be as high as 3.4 %. It also reveals that, for the human foot, the differences of ball width and ball angles between the loaded and the unloaded situation can be as large as 8 mm and 2 degrees, respectively. This highlights the importance of using 4D scan in designing garments and apparel.

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Predicting pediatric spinal deformity (PSD) from X-ray images collected on the patient’s initial visit is a challenging task. This work builds on our previous method and provides a novel bio-informed framework based on a mechanistic machine learning technique with dynamic patient-specific parameters to predict PSD. We provide a geometry-based bone growth model that can be utilized in a range of applications to enhance the bio-informed mechanistic machine learning framework. The proposed technique is utilized to examine and predict spine curvature in PSD cases such as adolescent idiopathic scoliosis. The best fit of a segmented 3D volumetric geometry of the human spine acquired from 2D X-ray images is employed. Using an active contour model based on gradient vector flow snakes, the anteroposterior and lateral views of the X-ray images are segmented to derive the 2D contours surrounding each vertebra. Using minimal user input, the snake parameters are calibrated and automatically computed over the dataset, resulting in fast image segmentation and data collection. The 2D segmented outlines of each vertebra are transformed into a 3D image segmentation result. The Iterative Closest Point mesh registration technique is then used to establish a mesh morphing approach and creates a 3D atlas spine model. Using the comprehensive 3D volumetric model, one can automatically extract spinal geometry data as inputs to the mechanistic machine learning network. Moreover, the proposed bio-informed deep learning network with the modified bone growth model achieves competitive or even superior performance against other state-of-the-art learning-based methods.Please check and confirm if the author names and initials are correct for “Yongjie Jessica Zhang” and “Wing Kam Liu”.We confirm they are correct.

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Analysing the contact area between head-related products and the corresponding craniofacial profile is necessary to make such products more comfortable. The study of soft tissue biomechanical experiments provides a reliable perspective for understanding such contact areas and improving the comfort. In order to obtain more accurate and visualized craniofacial biological information that can be used to guide product design, CT data of the head, face, and neck of 50 Chinese aged 18-35 years were obtained in this paper. For each subject, an individual thickness map is calculated by segmentation of the soft tissue layer and wall-thickness calculation via Mimics. The individual maps superimposed on the outer surface area of the head, face, and neck are brought into correspondence using a non-rigid iterative closest point technique. From the correspondence an average head, face, and neck geometry and soft tissue thickness map was calculated. Statistics of the overall soft tissue thickness of the head, face, and neck is extracted, and an accurate soft tissue thickness map of the Chinese head, face, and neck is generated. This study not only lays the groundwork for future simulation experiments on head-related product design, but it also has significant implications for the fields of facial reconstruction in China.@en

Non-invasive ventilation (NIV) is increasingly used in the support of acute respiratory failure in critically ill children admitted to the pediatric intensive care unit (PICU). One of the major challenges in pediatric NIV is finding an optimal fitting mask that limits air leakage, in particular for young children and those with specific facial features. Here, we describe the development of a pediatric head–lung model, based on 3D anthropometric data, to simulate pediatric NIV in a 1-year-old child, which can serve as a tool to investigate the effectiveness of NIV masks. Using this model, the primary aim of this study was to determine the extent of air leakage during NIV with our recently described simple anesthetic mask with a 3D-printed quick-release adaptor, as compared with a commercially available pediatric NIV mask. The simple anesthetic mask provided a better seal resulting in lower air leakage at various positive pressure levels as compared with the commercial mask. These data further support the use of the simple anesthetic mask as a reasonable alternative during pediatric NIV in the acute setting. Moreover, the pediatric head–lung model provides a promising tool to study the applicability and effectiveness of customized pediatric NIV masks in the future.

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Preserving features of a surface as characteristic local shape properties captured e.g. by curvature, during non-rigid registration is always difficult where finding meaningful correspondences, assuring the robustness and the convergence of the algorithm while maintaining the quality of mesh are often challenges due to the high degrees of freedom and the sensitivity to features of the source surface. In this paper, we present a non-rigid registration method utilizing a newly defined semi-curvature, which is inspired by the definition of the Gaussian curvature. In the procedure of establishing the correspondences, for each point on the source surface, a corresponding point on the target surface is selected using a dynamic weighted criterion defined on the distance and the semi-curvature. We reformulate the cost function as a combination of the semi-curvature, the stiffness, and the distance terms, and ensure to penalize errors of both the distance and the semi-curvature terms in a guaranteed stable region. For a robust and efficient optimization process, we linearize the semi-curvature term, where the region of attraction is defined and the stability of the approach is proven. Experimental results show that features of the local areas on the original surface with higher curvature values are better preserved in comparison with the conventional methods. In comparison with the other methods, this leads to, on average, 75\%, 8\% and 82\% improvement in terms of quality of correspondences selection, quality of surface after registration, and time spent of the convergence process respectively, mainly due to that the semi-curvature term logically increases the constraints and dependency of each point on the neighboring vertices based on the point's degree of curvature.@en

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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Personalized designs bring added value to the products and the users. Meanwhile, they also pose challenges to the product design process as each product differs. In this paper, with the focus on personalized fit, we present an overview as well as details of the personalized design process based on design practice. The general workflow of personalized product design is introduced first. Then different steps in the workflow such as human data/parameters acquisition, computational design, design for digital fabrication, and product evaluation are presented. Tools and methods that are often used in different steps in the process are also outlined where in human data acquisition, 3D scanning, and digital human models are addressed. For computational design, the use of computational thinking tools such as abstraction, decomposition, pattern recognition and algorithms are discussed. In design for digital fabrication, additive manufacturing methods (e.g. FDM), and their requirements on the design are highlighted. For product evaluation, both functional evaluation and usability evaluation are considered and the evaluation results can be the starting point of the next design iteration. Finally, several case studies are presented for a better understanding of the workflow, the importance of different steps in the workflow and the deviations in the approach regarding different contexts. In conclusion, we intend to provide designers a holistic view of the design process in designing personalized products as well as help practitioners trigger innovations regarding each step of the process.@en

Aerodynamic drag force can account for up to 90% of the opposing force experienced by a cyclist. Therefore, aerodynamic testing and efficiency is a priority in cycling. An inexpensive method to optimize performance is required. In this study, we evaluate a novel indoor setup as a tool for aerodynamic pose training. The setup consists of a bike, indoor home trainer, camera, and wearable inertial motion sensors. A camera calculates frontal area of the cyclist and the trainer varies resistance to the cyclist by using this as an input. To guide a cyclist to assume an optimal pose, joint angles of the body are an objective metric. To track joint angles, two methods were evaluated: optical (RGB camera for the two-dimensional angles in sagittal plane of 6 joints), and inertial sensors (wearable sensors for three-dimensional angles of 13 joints). One (1) male amateur cyclist was instructed to recreate certain static and dynamic poses on the bike. The inertial sensors provide excellent results (absolute error = 0.28?) for knee joint. Based on linear regression analysis, frontal area can be best predicted (correlation 0.4) by chest anterior/posterior tilt, pelvis left/right rotation, neck flexion/extension, chest left/right rotation, and chest left/right lateral tilt (p 0.01)..

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Current classifications of midshaft clavicle fractures are based on radiography. The aim of the study was to evaluate the fracture pattern of clavicle fractures using 3-dimensional computed tomography (3D CT). A retrospective analysis was performed on CT scans of 65 acute clavicle fractures. Using quantitative 3D CT reconstruction techniques, the fracture of the clavicle was virtually reduced. Based on these reconstructions, a group-based fracture heat map and small fragment heat map, and the location of the most common fracture line were determined. Also, the direction and amount of displacement were evaluated. Three fracture patterns could be distinguished. The primary fracture line in type 1 is going from posteromedial to anterolateral and located between 50% and 68% of the clavicle’s length. In type 2, a transverse fracture line is located around 55%, and in type 3, a superolateral to inferomedial line is located between 47% and 56%. Wedged fracture fragments can be seen in types 1 and 2 and are mainly situated inferiorly. The displacement is similar in all types, but the main direction of displacement is specific for the different types (posterior, anterior, inferior). We can conclude that several fracture patterns can be seen in clavicle fractures. Most fractures are located laterally at the midshaft of the clavicle. Wedged segments are mainly located inferiorly, and at the posterior part of the clavicle, no comminution is ever seen. The direction of displacement depends on the fracture pattern.@en

The use of 3D anthropometric data of children’s heads and faces has great potential in the development of protective gear and medical products that need to provide a close fit in order to function well. Given the lack of detailed data of this kind, the aim of this study is to map the size and shape variation of Dutch children’s heads and faces and investigate possible implications for the design of a ventilation mask. In this study, a dataset of heads and faces of 303 Dutch children aged six months to seven years consisting of traditional measurements and 3D scans were analysed. A principal component analysis (PCA) of facial measurements was performed to map the variation of the children’s face shapes. The first principal component describes the overall size, whilst the second principal component captures the more width related variation of the face. After establishing a homology between the 3D scanned face shapes, a second principal component analysis was done on the point coordinates, revealing the most prominent variations in 3D shape within the sample.

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