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The purpose of this paper is to present results of shape analysis of female torso shape using the discrete cosine transform (DCT) from a three-dimensional (3D) whole body scan database.

Torso shape is a central part of body shape and difficult to describe by linear measurements. In order to analyze body shape variation within a population the authors employed a DCT-based shape description method to compresses a dense 3D body scan surface into a small vector that preserves shape and removes size. The DCT-based shape descriptors of torso surfaces are further fed to principal component analysis (PCA) that decompose shape variation into constituent shape components. A visualization program was developed to observe principal components of torso shape and interpret their meanings.

Extreme shapes of the first ten principal components summarize major shape variations and identify shapes that are difficult to capture with traditional anthropometric measurements. PCA results also help to find and retrieve similar shapes from a population-level database.

Using the DCT for PCA of torso shape is a unique and original approach. It provides a basis for the description and classification of torso shape in 3D and the results from the shape analysis are potentially useful for designers of clothing and personal protective equipment.

We aimed to establish criteria for determining the deformed lateral torso type to identify individuals requiring measurement methods different from standard methods before extracting dimensions from three-dimensional (3D) scan data.

We collected the 3D body scan data of 119 women aged 70–85 years collected in the 6th Size Korea. Three axes were defined to determine the deformation of the lateral shape, and the angle of each reference axis was used for the analysis. Additionally, to classify the lateral torso shape, 14 experts made visual judgments on the side-view images of the participants.

To identify the axis that best represented the lateral torso shape, we used each angle value of the three reference axes as an independent variable and the expert’s visual classification as a dependent variable. Each discriminant function was obtained and accuracy calculated. The whole torso axis exhibited the highest accuracy. Next, an assessment scale was developed to determine the shape of the lateral torso using the angular value of the whole torso axis.

The scale developed in this study has the potential to reduce measurement errors arising from elderly deformed torso shapes, thereby enhancing data reliability.

The purpose of this paper is to provide algorithms of the automatic landmark extraction software program that are applicable for any torso shape.

In this study, Automatic Landmark Identification (AULID), an automatic landmark extraction software program, was developed to extract consistent landmark locations from any torso shape. A methodology of geometrical characteristics of the body surfaces around each landmark was used for the algorithms and implemented with C++. The accuracy of the AULID was tested on various torso shapes. The verification methodology consisted of mean difference (MD), mean absolute differences (MAD), and one‐way analysis of variance. Duncan test for multiple comparisons was used to evaluate the significant differences of MAD values among different torso groups. The MAD values were compared to the anthropometric survey allowable errors.

The algorithms of AULID provided both accuracy and consistency of identifying landmarks on any body torso types.

Most 3D body scanning systems often show landmark location errors when dealing with nonstandard body shapes. None of automatic landmark extraction software program provides consistency of identifying landmarks in various body shapes. However, algorithms of AULID, an automatic landmark extraction software program, in this study are only consistent definitions for identifying landmarks in any torso shape.

The purpose of this paper is to develop a virtual body that resembles the customer’s body shape using only the minimum information provided by the customer and without requiring individually scanned data.

The target of this study includes the three-dimensional scanned data of 91 senior women aged 60 or older and human body measurement data of 268 people. The parametric virtual body was generated in three steps: a basic virtual body, a trans-shaped virtual body, and a trans-sized virtual body.

Using organic relationships found in the body shape factors of the lower body, this study developed an algorithm to generate elderly women’s parametric virtual lower body that is quick and reproducible. Having tested the reproducibility of the algorithm, the parametric virtual body showed excellent reproducibility vis-à-vis the personal scanned data in both the shape acceptability and size acceptability.

Because virtual bodies in this study are based on the results of body shape analysis related to apparel design, those resembling customer body shapes can be quickly and accurately generated. In addition, because body shape information for target groups is provided to the clothing manufacturers, it will likely contribute significantly to enhancing clothes fitting.

The purpose of this paper is to analyze automation of body surface shape.

Numerous body landmarks are detected automatically. Body surface can be subdivided into multiple patches in a consistent manner using parametric design method.

Complex surface shape of various human bodies can be analyzed easily and consistently.

The proposed method may not be applicable for a body with the shape which significantly differs from that of an average body.

This method can greatly reduce the time required to analyze the surface shape of a three dimensional body scan data.

The analysis of body surface shape is one of the most important processes especially in designing close fitting garments. The parametric design of body surface patches will facilitate the analysis of numerous body scan data.

This research aimed to develop an automatic 3D body measurement line generation method that reduces errors induced by diverse body shapes and incomplete scan areas.

Three-dimensional body scan data from the 5th Size Korea database were used. Measurement extraction methods were developed for each measurement; chest girth, underbust girth, armscye girth and neck base girth.

The research showed that the method adopted in this study enhanced the accuracy of the scan measurements for various body shapes and incomplete scan data. The authors verified the accuracy of the developed methods for various body shapes by comparing the scan measurement against manual measurement.

The automatic 3D body measurement line generation algorithms developed for various human body shapes will improve the reliability and accuracy of 3D body scan measurement program. Also. it will be of practical use in human-size-related production processes.

The suboptimal fit of a spacesuit can interfere with a crewmember's performance and is regarded as a potential risk factor for injury. To quantify suit fit, a virtual fit assessment model was previously developed to identify suit-to-body contact and interference using 3D human body scans and suit CAD models. However, ancillary suit components and garments worn inside of the suit have not been incorporated.

This study was conducted to predict a 3D model of the liquid cooling and ventilation garment (LCVG) from an arbitrary person's body scan. A total of 14 subjects were scanned in a scan wear and LCVG condition. A statistical model was generated using principal component analysis and random forest regression technique.

The model was able to predict the geometry of the LCVG layer at the accuracy of 5.3 cm maximum error and 1.7 cm root mean square error. The errors were more pronounced for the arms and lower torso, while the thighs and upper torso regions, which are critical for suit fit assessments, show more accurate predictions. A case study of suit fit with and without the LCVG model demonstrated that the new model can enhance the scope and accuracy of future spacesuit assessments.

The capabilities resulting from these modeling techniques would greatly expand the assessments of fit of the garment on various anthropometries. The results from this study can significantly improve the design process modeling and initial suit sizing efforts to optimize crew performance during extravehicular activity training and missions.

This bibliography is offered as a practical guide to published papers, conference proceedings papers and theses/dissertations on the finite element (FE) and boundary element (BE) applications in different fields of biomechanics between 1976 and 1991. The aim of this paper is to help the users of FE and BE techniques to get better value from a large collection of papers on the subjects. Categories in biomechanics included in this survey are: orthopaedic mechanics, dental mechanics, cardiovascular mechanics, soft tissue mechanics, biological flow, impact injury, and other fields of applications. More than 900 references are listed.

The purpose of this paper is to quantitatively assess the three-dimensional (3D) geometry and symmetry of the torso for spinal deformity and the use of orthotic bracewear by using non-invasive 3D body scanning technology.

In pursuing greater accuracy of body anthropometric measurements to improve the fit and design of apparel, 3D body scanning technology and image analysis provide many more advantages over the traditional manual methods that use contact measurements. To measure the changes in the torso geometry and profile symmetry of patients with adolescent idiopathic scoliosis, five individuals are recruited to undergo body scanning both with and without wearing a rigid brace during a period of six months. The cross-sectional areas and profiles of the reconstructed 3D torso models are examined to evaluate the level of body symmetry.

Significant changes in the cross-sectional profile are found amongst four of the patients over the different visits for measurements (p < 0.05), which are consistent with the X-rays results. The 3D body scanning system can reliably evaluate changes in the body geometry of patients with scoliosis. Nevertheless, improvements in the symmetry of the torso are found to be somewhat inconsistent among the patients and across different visits.

This pilot study demonstrates a practical and safe means to measure and analyse the torso geometry and symmetry so as to allow for more frequent evaluations, which would result in effective and optimal treatment of spinal deformation.

The human body has the same basic size data but has different surface morphology, resulting in the unfitness even under the same size specification. The purpose of this study was to solve the local fitness problems by representing and quantifying the human surface morphological difference.

Firstly, the 3D point cloud for 323 female students was scanned, and the cross-section layers of the “waist-to-thigh” zone were determined. Secondly, the space vector based on the space Euclidean distance was extracted to represent and quantify the surface morphological difference. And the Principal Component Analysis and K-means were adopted to subdivide the target zone. Thirdly, the pattern based on the subdivision results and surface flattening was generated. Additionally, the fitness was evaluated by the subjective and objective assessments, separately.

The space vector could represent and quantify the shape morphology of the “waist-to-thigh” zone. It had successfully achieved the human body subdivision and corresponding pattern generation for the “waist-to-thigh” zone. And the pattern based on the shape subdivision and surface flattening of the space vector could effectively improve the wearing fitness. Particularly in the waist and crotch area of trousers, the obvious wrinkles had been solved because the space vector is more in line with the shape morphology characteristics.

The proposed method could represent and quantify the difference in human surface morphology in a 3D manner. It solved the unfitness problem caused by the same body size but different shape surface morphology. And it will contribute to the fitness improvement of the trousers.