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The purpose of this study was to categorize the whole body shapes of overweight and obese females in the US and examine apparel fit based on the current ASTM sizing standards related to the body shapes categorized.

Body scan data from 2,672 subjects were used. To categorize their whole body shapes using 97 body measurements, principal component analysis with varimax rotation, a hierarchical cluster analysis and K-means cluster analysis were used. To compare the ASTM sizing standards for plus sizes (curvy and straight) and missy sizes (curvy and straight), five body parts (bust, under bust, waist, top hip, hip) using the formula for fit tolerance (measurement plus half of the interval) were compared with the ASTM sizing standards to determine the size appropriate for each body part.

Five whole body shapes among overweight and obese females in the US were categorized: Rectangle-curvy; parallelogram-moderately curvy; parallelogram-hip tilt; inverted trapezoid-moderately curvy and inverted trapezoid-hip tilt. When the body measurements in each body shape were compared with the current ASTM sizing systems for both misses and plus sizes, four-fifths or more of overweight and obese female adults in the US would find it difficult to obtain a perfect fit for both tops and bottoms.

Identifying whole body shapes among overweight and obese women in the US contributes significantly, as it will help apparel companies that target the markets of larger women develop a new sizing system. This study is the first attempt to analyze fit by comparing the ASTM sizing charts with body measurements in each body shape group. Further, the study contributes to the body-related literature by filling gaps in missing whole BS categories among overweight and obese females.

This study focused on how to quantify the similarities of body shape based on the front and side images, and a shape comprehensive index (I_SC) of female upper body shape based on 2D images was proposed.

In total, 190 young women were shot for front and side images, and 18 shape parameters were automatically extracted, including seven angles and 11 ratio parameters. The coefficient of variation method was used to assign different weights for related parameters, and the I_SC was calculated to describe the body shape of each subject. Five cross-sectional curves of the upper body (e.g. shoulder, chest, waist, abdomen and hip) were selected for exploring the range of shape similarity.

According to the value of I_SC, if the difference among the subjects is within the range of ±0.02, their body shapes can be regarded as similar, and the subject with the minimum distance is considered as the most similar. Error results show that the error range of the angle parameter is from 0.2° to 3.6° and the ratio range is from 0.001 to 0.119. Moreover, the t-test value among the parameters of the similar body is above 0.05, indicating that there is no significant difference for the upper body shape of the similar groups.

This method can quantify body shapes with the upper body characteristics of young women instead of subjective judgment. The study can be extended to other parts of the body and can also provide a new thought for shape similarity retrieval based on 2D images.

Considering two-dimensional features in the body shape classification system cannot fully reflect the three-dimensional (3D) morphological characteristics of human body. The purpose of this paper is to propose a 3D feature based method to characterize and classify the upper body shape of women, and then obtained the corresponding garment block and improved the fitness of clothing.

In this study, the [TC]² 3D scanner was used to obtain human data, and 15 layers of cross-sections of young females’ upper body were extracted. In total, 240 space vectors were obtained with the center of the bust cross-section as the original point. By using the principal component analysis and K-means clustering analysis, the body shape classification based on the space vectors length was realized. The garment block corresponding to three body types was obtained using the 3D scanning data and the cross-section convex hull, and compared with existing garment block and evaluated fitness of the blocks.

In total, 11 main components used to characterize the 3D morphological features of young women were obtained, which could explain 95.28 percent features of young women’s upper body. By cluster analysis, the body shape of women was divided into three categories. The block of three body types was obtained by the construction of the convex hull model.

This paper investigates a classification method of the body shape based on space vector length, which can effectively reflect the difference of surface shape of human body and further improve the matching degree of human body and clothing.

With the rapid advancement of computer information technology, the traditional clothing industry has stridden towards automation and digitization that drive the growth of electronic commerce and line retailing. The purpose of this paper is to propose an approach on 3D upper body modelling based on the body measurements extracted by non-contact anthropometry.

Based on the frontal and side images of the human body, the body sizes were extracted through silhouette extraction, identification of landmarks and girth prediction. The generation rules of 15 characteristic cross-sectional curves were established using a method “feature points – inserted points – feature curves – basic surface – mannequin”. The feature points of each position were determined at each curve, such as the side neck point, front neck point, shoulder point, bust point, and bust root point and so on to get the cross-sections, and then some feature points were inserted at the curves according to the widths and depths to establish the calculative models. For example, there are 18 points distributed at the bust cross-sectional curve to determine the shape.

The final mannequin could describe the basic characteristics of a human body, and the shape of the feature curves could also fit the body type to provide basis for the future research on automatic pattern generation.

This study can realize the 3D virtual modelling of female upper body and the automatic generation of the individualized apparel patterns based on the frontal and side images.

Considering only two-dimensional (2D) ease allowance cannot fully reflect the three-dimensional (3D) relationship between the position of clothing and the human body. The purpose of this paper is to propose a method with a 3D space vector and corresponding distance ease to characterize fitting garments and then used to construct personalized clothing for similar shape body.

Firstly, a 3D scanner was used to obtain mannequin and fitted garment data, and 17 layers of cross-sections of the upper body were extracted. Then, 37 space vectors and corresponding space angles on each cross-section were obtained with the original point. Secondly, the detailed distance ease between the mannequin and garment was constructed due to the difference between garment vectors and body vectors. Thirdly, the distance ease mathematical models were achieved and used to calculate distance ease on a similar shape body. Additionally, the fit garment is constructed, and the garment pattern is altered by the geometric pattern alteration method.

The results show that 3D space vectors can explain the relationship between body skin and garment surface of the upper body properly. The distance ease is modeled by mathematic expressions and successfully used to make a new garment to fit a similar shape body.

The proposed method of constructing garments based on distance ease and 3D space vectors can create a fitted garment for a similar shape body effectively and accurately. It is useful for the personalized garment design and suitable for the manufacturing process.

The purpose of this paper is to focus on how to automatically generate the individualized patterns for women’s suits based on the 3D body point-cloud images.

With the software Imageware, the point-cloud data of the female body were measured according to the female body feature to obtain the heights, widths, depths and girths at various landmarks. Then the relationship between the height of each landmark and the body height was analyzed to build the height calculation rules by software SPSS, and the prediction models of body girths were established from the body widths and depths using regression analysis for pattern generation.

The pattern generation rules were built with the relationships between a human body and the garment patterns using the graphic flattening method. Based on the above rules, the final patterns were drafted automatically by using these dimensions to fit the subjects. The try-on experiment also showed that the individualized suits could fit the subjects’ body well at some feature landmarks.

In order to realize tailor-made and meet the consumers’ demands for individualized clothes, the development of garment CAD system has become inevitable in the garment industry. This paper could provide the foundation for automatic pattern generation, and technical support for tailor-made.

The purpose of this paper is to concentrate on the development of individualized prototype of apparel patterns for young females from 3D body scanning data.

The authors presented a new pattern-making approach that is composed of three major steps: to establish the relationships between body features and corresponding elements in a prototype (e.g. curve or a point); to classify the relationship into grades that provide alternatives to fit a variety of bodies; and to assemble each individual element into a personalized prototype.

The experiment demonstrated that this method could be used for customized prototype development from 3D body scanning in a relatively easy way.

Currently, the subjects of this study included only Chinese young females, and the regression models were just suitable for the similar body types though, the research method could be extended to other somatotypes and age groups.

This approach can be used in the field of made-to-measure, mass customization, and the quick response for apparel pattern making. The technology in this paper facilitates to generate an individualized pattern prototype from 3D body scanning data.

Originated from the relationship between the features of a human body and the elements of a pattern prototype, the authors presented a new approach to develop an individualized pattern prototype by classifying the features into grades.

The purpose of this paper is to provide footwear designers, manikin builders and thermo‐physiological modellers with sweat distribution information for the human foot.

Independent research from two laboratories, using different techniques, is brought together to describe sweat production of the foot. In total, 32 individuals were studied. One laboratory used running at two intensities in males and females, and measured sweat with absorbents placed inside the shoe. The other used ventilated sweat capsules on a passive, nude foot, with sweating evaluated during passive heating and incremental exercise to fatigue.

Results from both laboratories are in agreement. Males secreted more than twice the volume of sweat produced by the females (p<0.01) at the same relative work rate. Both genders demonstrated a non‐uniform sweat distribution, though this was less variable in females. Highest local sweat rates were observed from the medial ankles (p<0.01). The dorsal foot sweated substantially more than the plantar (sole) areas (p<0.01). Sweating on the plantar side of the foot was uniform. Wearing shoes limited the increase in sweat production with increasing load, while the sweat rate of uncovered feet kept increasing with work and thermal load.

The observed variation in sweat rate across the foot shows that footwear design should follow the body mapping principle. Fabrics and materials with different properties can be used to improve comfort if applied to different foot surfaces. The data also demonstrate that foot models, whether physical (manikins) or mathematical, need to incorporate the observed variation across the foot to provide realistic simulation/testing of footwear.

This paper reviews the pros and cons of different parametric modeling methods, which can provide a theoretical reference for parametric reconstruction of 3D human body models for virtual fitting.

In this study, we briefly analyze the mainstream datasets of models of the human body used in the area to provide a foundation for parametric methods of such reconstruction. We then analyze and compare parametric methods of reconstruction based on their use of the following forms of input data: point cloud data, image contours, sizes of features and points representing the joints. Finally, we summarize the advantages and problems of each method as well as the current challenges to the use of parametric modeling in virtual fitting and the opportunities provided by it.

Considering the aspects of integrity and accurate of representations of the shape and posture of the body, and the efficiency of the calculation of the requisite parameters, the reconstruction method of human body by integrating orthogonal image contour morphological features, multifeature size constraints and joint point positioning can better represent human body shape, posture and personalized feature size and has higher research value.

This article obtains a research thinking for reconstructing a 3D model for virtual fitting that is based on three kinds of data, which is helpful for establishing personalized and high-precision human body models.

This chapter provides a contemporary view of state-of-the science research and thinking done in the areas of selection and classification. It takes as a starting point the observation that the world of work is undergoing important changes that are likely to result in different occupational and organizational structures. In this context, we review recent research on criteria, especially models of job performance, followed by sections on predictors, including ability, personality, vocational interests, biodata, and situational judgment tests. The paper also discusses person-organization fit models, as alternatives or complements to the traditional person-job fit paradigm.