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The purpose of this study was to assess the body characteristics (body size, garment sizes worn, shape and body satisfaction) of Ethiopian young adult female consumers and their effects on garment fit satisfaction and fit preferences of ready-to-wear garments such as T-shirts, blouses/shirts, skirts and jeans trousers.

Through a structured questionnaire, 418 usable responses were collected from young female university students. One-way analysis of variance (ANOVA) and multivariate ANOVA (MANOVA) were used to examine differences in garment fit satisfaction with the body characteristics of the respondents. Moreover, a multiple regression analysis was used to determine the effect of body characteristics and fit satisfaction on fit preferences in four of the ready-to-wear garments.

The body shapes of above half of the respondents were either bottom hourglass or spoon. The respondents were somewhat dissatisfied with the fit of the ready-to-wear garments, and above 70 per cent of them faced fit problems on average. BMI showed significant fit satisfaction differences in four of the garments, while garment sizes worn were significant only in skirts. However, no statistically significant difference was obtained with body shape. The effect of body characteristics and fit satisfaction on fit preferences was found significant in T-shirts and jeans trousers. The respondents preferred looser shirts and skirts irrespective of their body characteristics.

The paper is unique in comprehensively examining the relationship between body characteristics and garment fit satisfaction and their effects on fit preferences in selected garments in the context of female consumers in a developing country.

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.

The purpose of this paper is to classify body shapes using angular defects instead of sizes.

A large amount of dimensional data from a national anthropometry survey was analysed, and a basic pattern and its polyhedron were also used to create a three‐dimensional body shape from three body sizes. Using this method, the sizes were converted into nine angular defects.

The authors could define the factors explaining body shape characteristics and classify the body shapes into four groups. The four groups could be characterised by two pattern making difficulties of the upper and lower parts of the body as well as by two proportions, of waist girth to bust girth and bust girth to back length. Furthermore, depending on the age, the authors could understand body shape by the angle made.

Using a polyhedron model, the angles could be calculated using an enormous existing data set of sizes. An angular defect serves as an index to indicate the degree of difficulty for developing a flat pattern. If an angular defect of the bust is large, it is difficult to make a paper pattern of a bust dart. On the other hand, if an angular defect of the waist is large, it is easy to make a paper pattern of a waist dart. Thus, each body shape could be simultaneously characterized by two difficulty indices and two proportions of sizes.

To develop a new method for computer‐based 3D construction of garment basic cut on a computer generated body model.

The method has been developed on an example of a 3D garment basic cut construction on a virtual body model, determining the position of characteristic 3D points necessary for computer‐based definition of 3D cutting pattern contour segments. Contour segments modelling, as well as the modelling of 3D cut surfaces has been done using the NURBS objects.

A 3D garment cut has been constructed, such that matches physical characteristics of the body in question and offers the necessary comfort of the cut. The surface of the 3D cut has been divided into individual 3D cutting patterns.

The method has been developed on an example of a 3D garment basic cut construction of a single paper of clothing. However, the same principles can be applied and developed for any garment basic cut.

The 3D garment cut constructed can be further transformed into a network of polygons. Introducing fabric physical‐chemical properties fabric drape can be simulated, aiming at more realistic visualisation and further assessment of the garment fit. The 3D cutting patterns developed can be, applying computer‐based application of the mathematical models, transformed into 2D cutting patterns.

As compared to the methods developed by some previous investigations, the newly developed method offers the construction of garment 3D cut on a computer‐generated body model, granting the necessary comfort of the cut, which also means garment fitted to individual body characteristics. The 3D cut constructed can also be used as a starting point to define 2D cutting patterns in the following step, which will be matched to the physical characteristics of the model body, in the same way as the initial 3D cut.

The purpose of this paper is to quickly acquire a cheongsam pattern using the fit quantification method to meet individual fit requirement.

Based on the cheongsam pattern database including basic patterns and graded patterns, we defined the main control parts of the cheongsam pattern by analyzing the pattern modification. Combining human body shape characteristics, this paper utilized the fuzzy membership function to quantify the cheongsam fit, and defined the modified model of the cheongsam control part.

The fitness quantification method can provide suitable primary body characteristics for custom-pattern and helps to produce customized cheongsam quickly.

This paper proposed a method of generating customized cheongsam pattern based on fitness quantification by using fuzzy membership function. The method combined the industry pattern design experience and mathematic knowledge to generate the individual fit pattern rapidly. It can be applied in cheongsam customization.

This study aims to investigate the apparel fit preferences of Mexican‐American women between the ages of 18 and 25 years old from the Southwestern USA. The study also seeks to analyze the effect of body shape perception, body mass index, and clothing size on apparel fit preferences.

Data were collected in an online survey using an original fit preference assessment scale. Sampling was restricted in terms of gender, age, subculture, and geography to control for the variability that exists in apparel preferences and the Hispanic market due to these factors. Descriptive and inferential statistics were used to describe the sample's fit preferences for casual pants, tops, skirts, and dresses and to determine whether physical body characteristics impact these preferences.

Overall, young Mexican‐American women preferred semi‐fitted apparel across all garment categories studied. Physical body characteristics tended to impact on the sample's preferences for close and loose‐fitting garments, with respondents who had narrower waists and smaller body sizes more likely to prefer close‐fitting garments. Respondents who had less defined waists and larger body sizes were more likely to prefer loose‐fitting garments.

Many apparel firms wish to create targeted products for the Hispanic consumer, given the substantial growth in the size and purchasing power of this market. However, firms have frequently had to rely on cultural stereotypes due to a lack of information. The study documented in the paper developed an original fit preference scale to obtain important information that can be used to impact on apparel product development for this consumer.

This study focused on how to realize automatic recognition of young women's neck-shoulder shape based on the front and side images.

The reverse engineering software was used to measure the body sizes of the neck-shoulder part based on the young women's three-dimensional (3D) point cloud data, and the important parameters closely related to the neck-shoulder shape were determined. The neck-shoulder shape of the subjects was classified to establish the classification rules. Then, based on the front and side images, the human body contour was extracted by Matlab, and the data required for neck-shoulder shape classification were obtained by identifying the feature points.

Through the cluster analysis based on the shoulder angle, back angle, shoulder depth/width ratio and armpit depth/width ratio, young women's neck-shoulder shape was divided into four categories, namely round wide shoulder, flat narrow shoulder, round drop shoulder and hunchback flat shoulder. The neck-shoulder shape could be automatically recognized based on the established classification rules and two-dimensional (2D) body measurement method, with an accuracy rate of 90%. The neck-shoulder shape automatic recognition system constructed based on this method is effective.

This study proposed a simple neck-shoulder automatic recognition method based on the 2D body images. This approach can be extended to other group of human body or other parts of the body.

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.

IT has been found by model tests that the lift of a combination of an aeroplane body and a wing is not in general equal to the sum of the lifts of the two components when tested separately. This difference is broadly referred to as an interference effect. It is dependent on a number of factors which can be generalised into three groups:—

The purpose of this paper is to identify the relationships between consumers' body types and characteristics such as shopping orientation, past experience with fit problems, attitudes toward internet shopping and attitudes toward 3‐D body scanning.

This study collected body data from 441 Korean females who participated in 3‐D body scanning. Using a hierarchical cluster analysis, the body scanning identified five body types. Then body types and shopping characteristics variables were analyzed.

Slim respondents showed significantly higher hedonic shopping orientation and their attitude to internet shopping was positive, since they evaluated using the internet as being “fun”. By contrast, respondents with larger body types considered the convenience of internet shopping to be the most important feature. Respondents, regardless of their body types, regarded 3‐D body scanning as an enjoyable, futuristic and interesting experience. Moreover, respondents showed a high inclination to spend more money for customization services which utilize the 3‐D scanning technique.

This study analyzed Korean females' body shapes using 3‐D scanned body measurements. This study can contribute to integrate knowledge in the body measurement sector and the consumer behavior sector.