The ergonomic fit of respiratory protective equipment (RPE) is critical for ensuring both protection and long-term usability in occupational settings. However, most respirators are designed based on outdated or foreign anthropometric data that may not represent local populations. In Chile, as in many countries without updated national databases, this mismatch can compromise comfort, effectiveness, and user compliance. This study evaluated temporal changes in facial dimensions among Chilean workers and assessed the applicability of four widely used respirator fit test panels. Two representative datasets collected a decade apart were analyzed: Dataset A (n = 474; 2013) with manual measurements, and Dataset B (n = 2016; 2024) using 3D facial scanning. Eleven facial dimensions recommended by ISO standards were examined against the LANL half- and full-facepiece panels and the NIOSH/ISO bivariate and PCA panels. Results showed significant increases in facial size, particularly among men, and a general shift toward larger facial morphologies. The NIOSH/ISO bivariate panel provided the highest coverage, while the LANL full-facepiece panel showed the poorest fit, especially among recent male participants. Gender-based differences in fit were consistent across both datasets. These findings underscore the need for updated, population-specific anthropometric references and the ergonomic redesign of respirators and fit panels. Although centered on Chile, the study has global relevance for countries that import RPE without validating fit locally. The methodology offers a scalable approach for aligning protective equipment with evolving worker characteristics, supporting international efforts to improve comfort, safety, and usability through data-informed design. These declining match rates suggest that respirator fit panels may become increasingly outdated, potentially compromising worker safety if they are not updated to reflect current population characteristics.

This study aimed to collect and analyze three-dimensional (3D) anthropometric data of Chilean workers to support the design of personal protective equipment (PPE) tailored to their physical characteristics. A total of 2016 participants, including Chileans and migrant workers, were measured using advanced 3D scanning technology. Significant sex-based and nationality-related differences were identified, with males exhibiting larger dimensions across most measurements. Comparisons with international datasets, including CAESAR, revealed unique anthropometric features among Chilean workers, highlighting substantial deviations in head and facial dimensions. These findings underscore the need for sex-specific and population-specific PPE designs, particularly given Chile's increasing workforce diversity. The results have practical implications for improving PPE fit, comfort, and safety in the workplace. This study provides a robust 3D anthropometric database that serves as a critical resource for manufacturers and safety professionals aiming to enhance occupational safety and equipment performance.

Background: Non-invasive ventilation is commonly used to support critically ill children with acute respiratory failure in the pediatric intensive care unit. However, non-invasive ventilation treatment is often hindered by poorly fitting masks due to limited commercially available options. Personalized non-invasive ventilation masks are a promising solution, yet research on the feasibility of their production in real-world clinical settings, particularly regarding facial data acquisition, remains limited. This study aims to assess the feasibility of using a handheld 3D scanner for facial data acquisition in critically ill children admitted to the pediatric intensive care unit. Methods: In this single-center pediatric intensive care unit feasibility study, facial 3D data was obtained from children (age 0–18 years) receiving non-invasive respiratory support for acute respiratory failure, using a handheld 3D scanner. Feasibility outcomes included the scan process and quality factors. Scan quality was evaluated based on scan errors and removed movement frames. Facial 3D data acquisition was defined as feasible if > 80% of patients had a complete scan whereof > 90% frames had a scan error < 0.5. Results: We included 33 patients with a median (IQR) age of 2.0 (1.0–16.0) months. Full facial 3D data could be acquired within a short scanning period of 30 s, which did not induce patient clinical deterioration, with a success rate of 31 (94%) usable scans with good quality (98% good frames). Conclusion: Our results show that facial data acquisition using a handheld 3D scanner is feasible in critically ill children receiving non-invasive respiratory support in the pediatric intensive care unit. These findings are essential for developing and implementing a workflow process for personalized non-invasive ventilation masks for children with acute respiratory failure.

Background:
Personal Protective Equipment (PPE) is crucial for minimizing workplace hazards, and its effectiveness relies on adapting to diverse anthropometric features.

Objective:
To establish the first 3D anthropometry database of Chilean workers. Also, this study compares 18 dimensions with the North American CAESAR three-dimensional anthropometrical scan database.

Methods:
The research utilized three-dimensional data collected from 348 Chilean individuals, ages ranging between 19 and 68 years old. Measurements were captured with a 3D face scanner (3dMD®) following ISO/TS 16976-2 and ISO 15535 guidelines to maintain rigorous standards.

Results:
Noticeable sexual dimorphism: Chilean males exhibit larger facial dimensions than females, such as Nose breadth and Face length, which emphasize the need for gender-specific PPE designs. Furthermore, comparisons with the CAESAR dataset revealed significant disparities among Chilean and other populations, emphasizing the importance of ethnic tailoring PPE. The implications for respiratory PPE design are substantial; variations in dimensions like Face length and Face width highlight the need for adjustable or size-specific respirators.

Conclusions:
The study underlines the importance of considering not only gender-specific differences but also ethnic variations in PPE design. The findings emphasize the critical role of anthropometric data in developing tailored respiratory protection devices, ensuring effective workplace safety across diverse populations. The study recommends further research to validate the findings in other populations and to advocate for inclusive design practices in occupational safety.

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.

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.

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.

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.

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.

Introduction: Foot shape assessment is important to characterise the complex shape of a foot, which is in turn essential for accurate design of foot orthoses and footwear, as well as quantification of foot deformities (e.g., hallux valgus). Numerous approaches have been described over the past few decades to evaluate foot shape for orthotic and footwear purposes, as well as for investigating how one’s habits and personal characteristics influence the foot shape. This paper presents the developments reported in the literature for foot shape assessment.

Method: In particular, we focus on four main dimensions common to any foot assessment: (a) the choice of measurements to collect, (b) how objective these measurement procedures are, (c) how the foot measurements are analyzed, and (d) other common characteristics that can impact foot shape analysis.

Results: For each dimension, we summarize the most commonly used techniques and identify additional considerations that need to be made to achieve a reliable foot shape assessment.

Discussion: We present how different choices along these two dimensions impact the resulting foot assessment, and discuss possible improvements in the field of foot shape assessment.

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.

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.

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.

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.

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).

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.

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.

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.

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.