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 However, most respirators are designed based on outdated or foreign anthropometric data that may not represent local populations.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 These declining match rates suggest that respirator fit panels may become increasingly outdated, potentially compromising worker safety if they are not updated to reflect current population characteristics.

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.

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.

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.

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.

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.

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).

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.