Body Shape Model Research Articles

Subject-specific trunk segmental masses prediction for musculoskeletal models using artificial neural networks.

Accurate determination of body segment parameters is crucial for studying human movement and joint forces using musculoskeletal (MSK) models. However, existing methods for predicting segment mass have limited generalizability and sensitivity to body shapes. With recent advancements in machine learning, this study proposed a novel artificial neural network-based method for computing subject-specific trunk segment mass and center of mass (CoM) using only anthropometric measurements. We first developed, trained, and validated two artificial neural networks that used anthropometric measurements as input to predict body shape (ANN1) and tissue mass (ANN2). Then, we calculated trunk segmental mass for two volunteers using the predicted body shape and tissue mass. The body shape model (ANN1) was tested on 279 subjects, and maximum deviation between the predicted body shape and the original was 28mm. The tissue mass model (ANN2) was evaluated on 223 subjects, which when compared to ground truth data, had a mean error of less than 0.51% in the head, trunk, legs, and arms. We also compared the two volunteer's trunk segment mass with experimental data and found similar trend and magnitude. Our findings suggested that the proposed method could serve as an effective and convenient tool for predicting trunk mass.

An Efficient Machine Learning Prediction Method for Vehicle Detection: Data Analytics Framework

The availability of transportation is considered a significant hallmark of a developed society. Since the evolution of the human species, the imperative to relocate from one location to another has been a fundamental requirement. At present, there exists a plethora of transportation options in Indonesia. However, most individuals favor road transportation due to its ease and convenience. The rise in population has led to a corresponding increase in the number of vehicles on the roadways. Hence, it presents a challenge for security authorities and governmental bodies to oversee all automobiles' mobility across various locations effectively. The present study proposes a methodology for detecting and tracking vehicles using video-based techniques. The process's initial stages involve preprocessing, including frame conversion and background subtraction. Next, the process of detecting vehicles involves the utilization of change detection and a model of body shape. Subsequently, the next stage entails the feature extraction process, focusing on extracting energy features and directional cosine. Subsequently, a technique for optimizing data is employed on the vector comprising excessively extracted features. The methodology integrates a data mining technique based on association rules, which is subsequently complemented by a random forest classification algorithm. The approach generally integrates multiple methodologies to attain effective and precise identification of automobiles in video-derived datasets.

Variables that explain disordered eating behaviors among women: the mediating role of body dissatisfaction

PurposeTo analyze the role of body dissatisfaction in the relationships of sociocultural influences, depression, and anxiety with disordered eating behaviors (DEB) in a sample of female Mexican university students.MethodsA nonrandom sample of 526 female Mexican university students aged 18 to 25 years completed the Questionnaire of Influence on the Aesthetic Model of Body Shape (CIMEC-26), the Hospital Anxiety and Depression Scale (HADS), the Body Shape Questionnaire (BSQ-8D) and the Eating Attitudes Test (EAT-26).ResultsThrough the mean model (χ2/df (5, n = 526) = 7.298, p = .199; NFI = .996; CFI = .999; RMSEA = .030; SRMR = .011), body dissatisfaction was found to mediate the relationships of influence of advertising, influence of social models and anxiety with DEB (restrictive dieting and bulimia). The variable with the most direct effect on restrictive dieting and bulimia was the influence of advertising. Body dissatisfaction partially mediated this relationship, as the influence of advertising had a significant direct effect on restrictive dieting and bulimia. The final model of direct and indirect effects explained 43% and 22% of the variance in restrictive dieting and bulimia, respectively.ConclusionThe present study showed that body dissatisfaction partially mediated the relationships between influence of advertising, influence of social models, and anxiety with DEB among women. Thus, these variables should be taken into account in prevention and intervention programs targeting BED. Level V: Evidence obtained from a cross-sectional descriptive study.Level VEvidence obtained from a cross-sectional descriptive study.

A LiDAR-less approach to autonomous hazard detection and avoidance systems based on semantic segmentation

In this paper, a passive hazard detection and avoidance (HDA) system is presented, relying only on images as observations. To process these images, convolutional neural networks (CNNs) are used to perform semantic segmentation and identify hazards corresponding to three different layers, namely feature detection, shadow detection, and slope estimation. The absence of active sensors such as light detection and ranging (LiDAR) makes it challenging to assess the surface geometry of a celestial body, and the training of the neural networks in this work is oriented towards coping with that drawback. The image data set for the training is generated using blender, and different body shape models (also referred to as meshes) are included, onto which stochastic feature populations and illumination conditions are imposed to produce a more diverse database. The CNNs are trained following a transfer learning approach to reduce the training effort and take advantage of previously trained networks. The results accurately predict the hazards in the images belonging to the data set, but struggle to yield successful predictions for the slope estimation, when images external to the data set are used, indicating that including the geometry of the target body in the training phase makes an impact on the quality of these predictions. The obtained predictions are composed to create safety maps, which are meant to be given as input to the guidance block of the spacecraft to evaluate the need for a manoeuvre to avoid hazardous areas. Additionally, preliminary hardware-in-the-loop (HIL) test results are included, in which the algorithms developed are confronted against images taken using real hardware.

A Purely Visual Re-ID Approach for Bumblebees (Bombus terrestris)

Entomologists have widely applied re-identification techniques to better understand insects and their interaction with the environment. While humans can re-identify other humans and some mammals quite well, entomologists rely on gluing markers on insects to perform this task. This paper presents an approach for purely visual re-identification of bumblebees (Bombus terrestris) without the need to use markers. Non-invasive identification methods offer the possibility to observe the interaction of bumblebees with their environment without disturbance. Both a CNN model and a simple body shape model were used to investigate how they can be re-identified within a colony. The best-performing model, BumbleNet, correctly identifies more than two-thirds (CMC-1 score) of the individuals. Bumblebees are known for their substantial variations in body shape. To understand whether other features can also play a role in re-identification, different augmentations are applied during the training of BumbleNet. It was found that non-body-shape features increased the performance of BumbleNet by 25 percentage points (CMC-1 score). This also explains the observed superiority of the CNN-based BumbleNet compared to the BumbleShape model, that is solely based on body size parameters.

Deep generation of 3D articulated models and animations from 2D stick figures

Generating 3D models from 2D images or sketches is a widely studied important problem in computer graphics. We describe the first method to generate a 3D human model from a single sketched stick figure. In contrast to the existing human modeling techniques, our method does not require a statistical body shape model. We exploit Variational Autoencoders to develop a novel framework capable of transitioning from a simple 2D stick figure sketch, to a corresponding 3D human model. Our network learns the mapping between the input sketch and the output 3D model. Furthermore, our model learns the embedding space around these models. We demonstrate that our network can generate not only 3D models, but also 3D animations through interpolation and extrapolation in the learned embedding space. In addition to 3D human models, we produce 3D horse models in order to show the generalization ability of our framework. Extensive experiments show that our model learns to generate compatible 3D models and animations with 2D sketches.

Vehicle Motion Planning With Joint Cartesian-Frenét MPC

The Frenét frame is commonly used in motion planning for its superiority of reshaping nonconvex curving boundaries and decoupling lateral and longitudinal behaviors. Nevertheless, such frame is not friendly for describing vehicle-related characteristics such as body shape and classical dynamic models. By contrast, the Cartesian frame facilitates straightforward modeling of vehicle dynamics, but confronts nonconvex constraints on curving lanes. For purpose of combining the complementary superiority of both frames, this letter proposes a joint Cartesian-Frenét MPC planning method based on a locally linear intermediary connection which is updated along with solving iterations. The augmented Cartesian-Frenét state space is constrained by a Cartesian vehicle control model that stands for tracking feasibility, a set of Frenét road constraints that account for driving safety, and a Cartesian-Frenét transformation that connects both frames. The final optimization is solved iteratively, where each iteration formulates into a quadratic programming problem with help of the apriori trajectory solved by the previous iteration. A comparative simulation and a 5-month-long real-vehicle test prove that the proposed method can produce a feasible and smooth trajectory within a safe area efficiently in complex real-world scenarios.

A device-agnostic shape model for automated body composition estimates from 3D optical scans.

Many predictors of morbidity caused by metabolic disease are associated with body shape. 3D optical (3DO) scanning captures body shape and has been shown to accurately and precisely predict body composition variables associated with mortality risk. 3DO is safer, less expensive, and more accessible than criterion body composition assessment methods such as dual-energy X-ray absorptiometry (DXA). However, 3DO scanning has not been standardized across manufacturers for pose, mesh resolution, and post processing methods. We introduce a scanner-agnostic algorithm that automatically fits a topologically consistent human mesh to 3DO scanned point clouds and predicts clinically important body metrics using a standardized body shape model. Our models transform raw scans captured by any 3DO scanner into fixed topology meshes with anatomical consistency, standardizing the outputs of 3DO scans across manufacturers and allowing for the use of common prediction models across scanning devices. A fixed-topology body mesh template was automatically registered to 848 training scans from three different 3DO systems. Participants were between 18 and 89 years old with body mass index ranging from 14 to 52kg/m2 . Scans were registered by first performing a coarse nearest neighbor alignment between the template and the input scan with an anatomically constrained principal component analysis (PCA) domain deformation using a device and gender specific bootstrap basis trained on 70 seed scans each. The template mesh was then optimized to fit the target with a smooth per-vertex surface-to-surface deformation. A combined unified PCA model was created from the superset of all automatically fit training scans including all three devices. Body composition predictions to DXA measurements were learned from the training mesh PCA coefficients using linear regression. Using this final unified model, we tested the accuracy of our body composition models on a withheld sample of 562 scans by fitting a PCA parameterized template mesh to each raw scan and predicting the expected body composition metrics from the principal components using the learned regression model. We achieved coefficients of determination (R2 ) above 0.8 on all nine fat and lean predictions except female visceral fat (0.77). R2 was as high as 0.94 (total fat and lean, trunk fat), and all root-mean-squared errors were below 3.0kg. All predicted body composition variables were not significantly different from reference DXA measurements except for visceral fat and female trunk fat. Repeatability precision as measured by the coefficient of variation (%CV) was around 2-3x worse than DXA precision, with visceral fat %CV below 2x DXA %CV and female total fat mass at 5x. Our method provides an accurate, automated, and scanner agnostic framework for standardizing 3DO scans and a low cost, radiation-free alternative to criterion radiology imaging for body composition analysis. We published a web-app version of this work at https://shapeup.shepherdresearchlab.org/3do-bodycomp-analyzer/ that accepts mesh file uploads and returns templated meshes with body composition predictions for demo purposes.

A Matching Degree Management Model of Human Body Shape and Fashion Design Based on Big Data Analysis

The existing clothing design model lacks the screening link of the human body part index, and the output clothing data are affected by the high correlation coefficient, resulting in large matching errors. Therefore, based on the analysis of human body shape, a management model of matching degree of human body shape and clothing design based on big data is constructed. After processing with big data methods, human body characteristic data used signals as the input layer of a neural network model and the matching degree management model of human body shape and fashion design. The simulation results show that the built-up model has a matching error of less than 5%, which can effectively improve the matching of human body shape and clothing design.

A Parametric Modeling of Adult Body Shape in a Supported Seated Posture Including Effects of Age

ABSTRACT Statistical body shape models (SBSM) provide compact, flexible representations of body shape that can be implemented in design software. However, few SBSMs have been created to represent adults in supported seated postures that are relevant for the design of seated environments, and none has incorporated the effects of age. This paper presents an SBSM based on surface laser-scan data from 155 U.S. adults. The data were processed to obtain homologous mesh structure and symmetric geometry, and the processed data were statistically analyzed using principal component analysis to obtain a compact representation of the data variance. Regression analysis was conducted to predict body size and shape from stature, body mass index, ratio of sitting height to stature, sex, and age. The resulting model allows rapid generation of realistic body models for applications, including product design, accommodation assessment, and safety system optimization. The model is publicly accessible at HumanShape.org. PRACTITIONER SUMMARY: This paper presents a statistical model that represents adult body shapes in a supported seated posture based on 3D anthropometric measurements. This model is the first whole-body parametric model known to incorporate age effects based on data extending beyond 65 years of age.

Capturing detailed deformations of moving human bodies

We present a new method to capture detailed human motion, sampling more than 1000 unique points on the body. Our method outputs highly accurate 4D (spatio-temporal) point coordinates and, crucially, automatically assigns a unique label to each of the points. The locations and unique labels of the points are inferred from individual 2D input images only, without relying on temporal tracking or any human body shape or skeletal kinematics models. Therefore, our captured point trajectories contain all of the details from the input images, including motion due to breathing, muscle contractions and flesh deformation, and are well suited to be used as training data to fit advanced models of the human body and its motion. The key idea behind our system is a new type of motion capture suit which contains a special pattern with checkerboard-like corners and two-letter codes. The images from our multi-camera system are processed by a sequence of neural networks which are trained to localize the corners and recognize the codes, while being robust to suit stretching and self-occlusions of the body. Our system relies only on standard RGB or monochrome sensors and fully passive lighting and the passive suit, making our method easy to replicate, deploy and use. Our experiments demonstrate highly accurate captures of a wide variety of human poses, including challenging motions such as yoga, gymnastics, or rolling on the ground.

Capturing detailed deformations of moving human bodies

We present a new method to capture detailed human motion, sampling more than 1000 unique points on the body. Our method outputs highly accurate 4D (spatio-temporal) point coordinates and, crucially, automatically assigns a unique label to each of the points. The locations and unique labels of the points are inferred from individual 2D input images only, without relying on temporal tracking or any human body shape or skeletal kinematics models. Therefore, our captured point trajectories contain all of the details from the input images, including motion due to breathing, muscle contractions and flesh deformation, and are well suited to be used as training data to fit advanced models of the human body and its motion. The key idea behind our system is a new type of motion capture suit which contains a special pattern with checkerboard-like corners and two-letter codes. The images from our multi-camera system are processed by a sequence of neural networks which are trained to localize the corners and recognize the codes, while being robust to suit stretching and self-occlusions of the body. Our system relies only on standard RGB or monochrome sensors and fully passive lighting and the passive suit, making our method easy to replicate, deploy and use. Our experiments demonstrate highly accurate captures of a wide variety of human poses, including challenging motions such as yoga, gymnastics, or rolling on the ground.

Automation of 3D average human body shape modeling using Rhino and Grasshopper Algorithm

The aim of this study is to develop an automated process for modeling average 3D human body according to body types using both NUBRS-based modeling software Rhinoceros 3D® (Rhino) and Grasshopper as an algorithm editor. First, we categorized men aged 36 to 55 years included in SizeUSA 3D data into the three body types (normal, overweight, and obese), and selected seven samples in each body type. To execute the automated process of generating an average 3D model of their lower bodies in a step-by-step manner, the following procedures were performed: (1) Determine the main reference lines on the 3D-scanned lower bodies, including six horizontal reference lines and six vertical reference lines; (2) Create horizontal and vertical line grids and intersection points (3) Generate an average 3D model in a position that corresponds to the average coordinates of the intersection points (vertex coordinates) of seven samples for each body type. A Grasshopper algorithm was formulated to automatically execute all procedures that had to be repeatedly performed. As a way to verify the average model’s size and shape, the girth measurements of the samples for each body type were averaged, and the results were compared with those of the 3D average body shape. It was found that the deviation was less than 1 cm, which indicates the validity of the 3D modeling approach applied in the present study. Each process was incorporated into commands available in the Rhino interface, and this automation allowed a number of 3D body shape modeling operations to be implemented in a significantly reduced time period.

Obesity effects on pedestrian lower extremity injuries in vehicle-to-pedestrian impacts: A numerical investigation using human body models

Objective The objectives of this study were to develop a method for modeling obese pedestrians and to investigate effects of obesity on pedestrian impact responses and injury outcomes. Methods The GHBMC (Global Human Body Model Consortium) 50th percentile male pedestrian model was morphed into geometries with 4 body mass index (BMI) levels (25/30/35/40 kg/m2) predicted by statistical body shape models. Each of the 4 morphed models was further morphed from a standing posture into 2 other gaits (toe-off and mid-swing), which resulted in a total of 12 (4 BMIs × 3 postures) models. Each model was used to simulate vehicle-to-pedestrian impact under 2 impact velocities. Pedestrian kinematics and injury measures were analyzed focusing on lower extremities. Statistical analyses were performed to examine significance of obesity on concerned injury measures. Results Peak values of the bending moment at tibia, force at medial collateral ligament (MCL), bending angle at knee joint, and contact force between vehicle and pedestrian increased significantly (P < .05) with increased BMI. By analyzing kinematics of the lower extremity, the overall vehicle-to-pedestrian impact was divided into 2 phases: “initial contact” and “tibia rebound.” For obese pedestrians, the added mass caused a higher tibia bending moment in the initial contact phase, and the greater moment of inertia led to greater bending angle and MCL force in the tibia rebound phase. Statistical analyses also revealed that pre-impact posture and impact velocity had significant effects on all injury measures. Conclusions Obesity could significantly increase the risk of pedestrian lower extremity injuries due to the inertial effect from the added mass. Pre-impact posture and impact velocity also significantly affect pedestrian injury measures. Future vehicle designs for pedestrian protection should consider populations with obesity. This study demonstrated the feasibility of using parametric human modeling to account for population diversity in injury prediction.

Statistical Human Body Shape Model including Elderly People.

In this study, we present a human body shape statistical model including elderly people, which is constructed using principal component analysis (PCA) on 3D body scan data of approximately 130 people. As a pre-process step, a template human body mesh model is fitted to 3D scan data using a coarse-to-fine surface registration technique based on a conformal deformation method, in order to establish correspondences between the scans of different subjects possibly in different poses. To change body style by a small set of parameters, such as "age", "weight" and "height" or the easily measurable anthropometric parameters like "shoulder width", the linear transformations between these attributes and the first 10 principal component scores are obtained. We design a simple user interface to use this deformation model to generate different body styles easily. As a result, we were able to produce and show body styles capturing the characteristics of elderly people whose shoulders fell and back bent. Finally, as an application, we used our deformation method to generate different body types, performed forward dynamics simulations in an assistive device setting and visualized the differences in contact pressure distributions due to body shape changes.

Model-based characterisation of vehicle occupants using a depth camera

Due to recent advances in sensing technologies, modern vehicle occupant classification systems enable personalised vehicle experiences and adaptive occupant crash protection. However, most systems are limited to occupant detection and simple classification, and thus, accurate estimation of body characteristics is needed to support more advanced occupant classification. This paper presents a model-based characterisation method for vehicle occupants using a 3D depth camera. This method automatically estimates standard anthropometric data of an occupant such as stature and weight along with the body shape by fitting a statistical body shape model to depth image data. The system is even robust to a wide range of clothing and is capable of generating accurate results. A variety of other algorithms were developed to improve the fitting result, including seat geometry detection and head location estimation. The new capability has a range of potential applications for improving occupant safety and providing an optimised interior configuration for the occupant.

Validating diverse human body models against side impact tests with post-mortem human subjects

This study aimed at evaluating the ability of morphed finite element (FE) human body models (HBMs) to reproduce the impact responses of post-mortem human subjects (PMHS) with various stature and shape. Ten side impact tests previously performed using seven PMHS under 3 m/s and 8 m/s impact velocities were selected for model evaluation. With weight, stature, sex, and age of PMHS, seven FE HBMs were developed by morphing the midsize male THUMS model into the target geometries predicted by the statistical skeleton and external body shape models. The model-predicted force histories, accelerations along the spine, and deflections in the chest and abdomen were compared to the test data. For comparison, simulations in all testing conditions were also conducted with the original midsize male THUMS, and the results from the THUMS simulations were scaled to the weight and stature from each PMHS. The CORrelation and Analysis (CORA) was used to evaluate the model accuracy, with CORA scores close to one indicating excellent agreement. Ten simulations using the morphed models exhibited 0.80 ± 0.01, 0.80 ± 0.01, 0.78 ± 0.02, and 0.78 ± 0.02 CORA scores for the impact forces to the thorax, abdomen, iliac-wings, and greater-trochanter, respectively; the corresponding CORA scores with the original THUMS were markedly lower at 0.60 ± 0.06, 0.69 ± 0.05, 0.71 ± 0.05, and 0.69 ± 0.04; while those for the scaled THUMS were 0.65 ± 0.05, 0.71 ± 0.05, 0.73 ± 0.05, and 0.72 ± 0.02, also lower than the morphed models. Across all simulations, the morphed HBMs demonstrated significantly higher accuracy than the THUMS with or without scaling. These results suggested the necessity of accounting for size and shape effects on predicting human responses in side impacts.

Small body shapes and spins reveal a prevailing state of maximum topographic stability

Over the past thirty years, spacecraft missions, Earth-based radar experiments, and telescopic observations have revolutionized our knowledge of Main Belt asteroids, near-Earth asteroids, and comet nuclei. As a result of this effort, we now possess high resolution shape models and well-constrained spin rates, pole orientations, and basic surface properties for a few dozen such small bodies. Here we present the results of a geomorphological examination of 32 such small body shape models along with their associated spin properties, and show that small body shape, gravity, and spin combine to gradually drive the surface towards a condition of maximum topographic stability; that is, a state of low topographic variation, low slopes, and low surface erosion (mass-wasting) rates. Of the 32 bodies investigated, 15 (47%) reside within this ‘zone of maximum topographic stability’, and when asteroid lightcurve-derived rotation rate and body elongation estimates are included, 1941 (70%) of 2791 well-observed asteroids reside within this zone. This finding indicates that given a mobile surface layer and sufficient time, small body surfaces naturally tend to erode, and their spin states gradually evolve, towards a state of maximum topographic stability, which also corresponds to a state of lowest internal stress. This erosional effect is most prominent on bodies several kilometers and larger in diameter, where YORP induced spin-state changes are small.

A Novel Self-Intersection Penalty Term for Statistical Body Shape Models and Its Applications in 3D Pose Estimation

Statistical body shape models are widely used in 3D pose estimation due to their low-dimensional parameters representation. However, it is difficult to avoid self-intersection between body parts accurately. Motivated by this fact, we proposed a novel self-intersection penalty term for statistical body shape models applied in 3D pose estimation. To avoid the trouble of computing self-intersection for complex surfaces like the body meshes, the gradient of our proposed self-intersection penalty term is manually derived from the perspective of geometry. First, the self-intersection penalty term is defined as the volume of the self-intersection region. To calculate the partial derivatives with respect to the coordinates of the vertices, we employed detection rays to divide vertices of statistical body shape models into different groups depending on whether the vertex is in the region of self-intersection. Second, the partial derivatives could be easily derived by the normal vectors of neighboring triangles of the vertices. Finally, this penalty term could be applied in gradient-based optimization algorithms to remove the self-intersection of triangular meshes without using any approximation. Qualitative and quantitative evaluations were conducted to demonstrate the effectiveness and generality of our proposed method compared with previous approaches. The experimental results show that our proposed penalty term can avoid self-intersection to exclude unreasonable predictions and improves the accuracy of 3D pose estimation indirectly. Further more, the proposed method could be employed universally in triangular mesh based 3D reconstruction.

Development of a three-dimensional body shape model of young children for child restraint design

The design of child restraints is guided in part by anthropometric data describing the distributions of body dimensions of children. However, three-dimensional body shape data have not been available for children younger than three years of age. This study presents body shape models for children weighing 9–23 kg in a seated posture relevant to child restraint design. A laboratory study collected surface geometry data of 67 children, ages 12–58 months. Novel template fitting methods were employed to obtain homologous meshes and to standardize the posture. Principal component analysis and regression were used to develop a statistical body shape model (SBSM). The SBSM was exercised to create 18 manikins representing children aged 1–3 years, with varying size and shape. These manikins will be useful for assessing child accommodation in restraints. The SBSM can also provide guidance for the development of anthropomorphic test devices and computational models of child occupants.