Clothing Size Research Articles

The influence of self-construal on consumer responses to sizing discrepancy

PurposeThis study examines how consumers' self-construal moderates consumers' buying behavior in situations requiring consumers to buy larger-than-expected clothing sizes. The authors explore the potential effectiveness of two distinct communication strategies – emotional versus informational ad appeals – to mitigate the negative effects of sizing discrepancies.Design/methodology/approachA total of three experiments were conducted to examine the proposed framework. Studies 1 and 2 investigate whether self-construal moderates the relationship between sizing discrepancy and purchasing intentions. Study 3 examines the effectiveness of communication strategies in reducing the detrimental effects of sizing discrepancy.FindingsWhen encountering sizing discrepancies, the authors find that consumers with an interdependent self-construal have lower purchase intentions than those with an independent self-construal. The authors demonstrate that an emotional communication strategy is more effective for consumers with an interdependent self-construal, whereas an informational communication strategy is more effective for consumers with an independent self-construal.Originality/valueWith the lack of a universal sizing system, consumers often struggle to find clothes that fit as expected. However, extant research has not explored cross-cultural differences in how consumers respond to sizing discrepancies and how managers can reduce any potential negative effects.

Enhancing human parsing with region‐level learning

AbstractHuman parsing is very important in a diverse range of industrial applications. Despite the considerable progress that has been achieved, the performance of existing methods is still less than satisfactory, since these methods learn the shared features of various parsing labels at the image level. This limits the representativeness of the learnt features, especially when the distribution of parsing labels is imbalanced or the scale of different labels is substantially different. To address this limitation, a Region‐level Parsing Refiner (RPR) is proposed to enhance parsing performance by the introduction of region‐level parsing learning. Region‐level parsing focuses specifically on small regions of the body, for example, the head. The proposed RPR is an adaptive module that can be integrated with different existing human parsing models to improve their performance. Extensive experiments are conducted on two benchmark datasets, and the results demonstrated the effectiveness of our RPR model in terms of improving the overall parsing performance as well as parsing rare labels. This method was successfully applied to a commercial application for the extraction of human body measurements and has been used in various online shopping platforms for clothing size recommendations. The code and dataset are released at this link https://github.com/applezhouyp/PRP.

Preserving cultural heritage through virtual simulation design of traditional clothing styles: a study of Mazu clothing

This study focuses on the virtual simulation design of Mazu clothing, a traditional Chinese style of clothing, using a combination of general design software, clothing CAD software, and clothing 3D design software. The study begins with an introduction to the development and shape of Mazu clothing and analyzes the design elements such as style, structure, color, and material of the clothing. Typical styles of Mazu clothing are then selected, and graphic designs for these styles are created using Corel Draw. The study then moves on to the virtual simulation design process, where the size and specifications of Mazu clothing are set based on the 160/84A standard female body data. Richpeace clothing CAD software is used to draw the pattern of Mazu clothing, completing the pattern design process. Finally, the plate file is imported into CLO 3D software to complete the 3D virtual stitching and simulation design of Mazu clothing. The study finds that the standard error and error probability after design were both below 5%, demonstrating the accuracy of the virtual simulation design method. The research plan presented in this study provides a fast and efficient way to achieve three-dimensional virtual simulation of Mazu clothing, which lays a foundation for the digital design and application of Mazu clothing. Overall, this study provides valuable insights and practical guidance for those interested in virtual simulation design for traditional clothing styles.

Impact of Clothing Size on Thermal Insulation – A Pilot Study

Abstract Thermal insulation may be influenced by the size of clothing and thus the volume of air gaps. The aim of this study was to determine the relationship between the size of outer wear clothing, and thus the indirect fit (the volume and size of air gaps), and thermal insulation in static and dynamic conditions. A set of underwear and two types of outerwear for workers of the energy sector and the chemical industry were selected for the study. Results showed that the value of thermal insulation (regardless of the type of outerwear) first increased with increasing clothing size.

KNOWLEDGE BASED RECOMMENDATION MODELING FOR CLOTHING PRODUCT SELECTION RECOMMENDATION SYSTEM

One of the markets which is the largest wholesaler of clothing in Central Java is the Klewer market area and its surroundings. The Simple Inc Store is one of the clothing stores in the area. Simple Inc Store is a large kiosk that sells clothing products in the form of various types of t-shirts, shirts, jackets, sweaters and pants. Sales of products in these stores are still done conventionally, namely customers come directly to the store to choose and buy products. The number of clothing products that are sold makes customers experience difficulties in the process of selecting clothing products. Therefore, it is necessary to develop a recommendation system that can assist customers in choosing clothing products. The purpose of this research is to create a Knowledge Based Recommendation model for the Recommendation System for Selection of Clothing Products. The system development method used in this research is Rapid Application Development (RAD), which consists of stages, namely business modeling, data modeling and process modeling. The recommendation system method used in this research is knowledge based recommendation. Knowledge based recommendation has the advantage of being able to set the level of user priority based on the user's needs for the product by calculating the similarity value between customer needs and clothing product attributes. Knowledge based recommendation modeling for this clothing product selection recommendation system can provide 5 choices of clothing product search attributes, namely brand, price, material, color and size. Based on the results of modeling the knowledge based recommendation method with 20 sample data, it can provide recommendations for clothing products based on the criteria needed by customers by calculating the similarity value between customer needs and the attributes possessed by each clothing product. Clothing products with the highest similarity value will be displayed as a result of clothing product recommendations, namely the highest similarity value of 0.6 is obtained for Maternal t-shirts. The results of this knowledge-based recommendation modeling can be used as a reference in developing a recommendation system for the selection of clothing products.

Predicting User’s Measurements without Manual Measuring: A Case on Sports Garment Applications

As sports garments are stretchable, different sizing tables are used than for retail clothing. However, customers measuring themselves leads to errors and unsatisfaction, since these customized branded garments cannot be returned. Using fitting sets avoids this, but this is not always feasible, especially in an online retail environment. Therefore, this research aims to use descriptive measures—parameters that do not require manual measuring because they are readily known by heart by almost any customer—to predict users’ body measurements, which can, thus, be used by customers to determine the size of their sports garment from a sizing chart. To validate if these input measures are sufficient to predict the correct size, three prediction methods are used and compared with baseline manual measurements. The methods are: (i) clothing size predictions from shape models with descriptive measures as inputs, (ii) clothing size predictions from a regression analysis, and (iii) clothing size predictions from a shape model based on extensive 3D scanned measurements as input. The conclusion is that a regression algorithm with, as input variables, the straightforward demographics of age, gender, stature, and weight is more accurate than the algorithm with the same inputs but with a shape model behind it. Moreover, chest and hip circumferences have an intraclass correlation coefficient rating above 0.9 and are, thus, suited for online retail of stretchable garments, such as cycling clothes. As validated by end-users, the regression predictions are shown to agree with preferred garment sizes of the participants, within the natural variation of personal preferences.

An anthropometric study to develop clothing charts for seventh, eighth and ninth grades of Benghazi schoolchildren

A sizing system classifies a specific population into homogeneous subgroups based on some key body dimensions. Individuals in the same subgroup have the same body shape characteristics, and share the same garment size. Anthropometric data plays an important role in creating clothing sizing system. The current project is the third step towards the overall objective to develop a clothing sizing system for Libyan children based on anthropometric body measurements of Libyan schoolchildren. The aim of the current project is to collect, examine and analyze anthropometric measurements for students in grades seven, eight and nine in the basic education stage, using simple statistical methods, in order to understand the body ranges and variations to develop sizing system for these grades. ANOVA tests were used to identify differences between age groups. The results showed that there are differences between most of the body measurements except the waist circumference, chest circumference, arm circumference, elbow circumference, shoulder length, front body width and back body width. Pearson correlation coefficients analysis was carried out to determine the interrelationships between the various body measurements. These findings showed that the weight and waist circumference were very strongly correlated with some other dimensions. The mean values and the standard deviation were used for creating size steps for the size chart. Three kinds of sizes were identified: L (large), M (medium) and S (small).

CLOTHING PRODUCT SELECTION RECOMMENDATION SYSTEM WITH KNOWLEDGE BASED RECOMMENDATION METHOD

The market which is the largest wholesaler of clothing in Central Java is the Klewer market area and its surroundings. One of the clothing stores in the area is the Simple Inc Store. Simple Inc Store is a large kiosk that sells clothing products in the form of various types of shirts, t-shirts, jackets, sweaters and pants. Sales of products in these stores are still done conventionally, namely customers come directly to the store to choose and buy products. The number of clothing products that are sold makes customers experience difficulties in the process of selecting clothing products. Therefore, it is necessary to develop a recommendation system that can assist customers in choosing clothing products. The purpose of this study is to build a Recommendation System for Selection of Clothing Products by applying the Knowledge Based Recommendation method. The research method used in this research is Rapid Application Development (RAD) which consists of 5 stages, namely Business Modeling, Data Modeling, Process Modeling, Application Generation, and Testing. Knowledge based recommendation has the advantage of being able to set the level of user priority based on the user's needs for the product. Knowledge based recommendation on the recommendation system for the selection of clothing products can provide 5 choices of search attributes for clothing products, namely brand, price, material, color and size. clothing product selection recommendation system can display clothing product information, perform clothing searches based on customer needs based on a choice of 5 attributes and can display clothing product recommendations. Clothing products with the highest similarity value are displayed as clothing product recommendations. The results of the system testing using the blackbox testing method show that the functions in the recommendation system for selecting clothing products have successfully run as expected.

METHOD OF VERIFICATION OF STANDARD ANTHROPOMETRICAL SURVEYS FOR CLOTHING

The performed research of normative documents made it possible to quantify the technical conditions of anthropometric body measurements and to determine how the nomenclature of anthropometric measurements depends on the classification of clothing. The aim of the study was to develop a methodology for verifying anthropometric measurements of the human body, taking into account the functional characteristics of clothing. A criteria approach to the synchronization of verification means of standards identification of measurement names by measurement method and measurement classification groups (horizontal, vertical, others) was used. Validation of standard measurements for the ergonomic design of garments and harmonization of control measurement code designations in finished garments have been performed. Standard control measurements were interpreted into additional dynamic compliance measurements to take into account the level of deformation of parts during operation and to ensure freedom of movement. Technical regulation of the anthropometric information volume is confirmed by the equivalence of the optimization problems of the considered anthropometric standards in the form of the linear programming problem of the set of anthropometric measurements. The matrix of conditions for fixing anthropometric measurements in six regulatory documents was studied. It was established that the set pair 25 < 28 determines the technical conditions of quality control on the dummy of the finished product. Using basic and auxiliary reference measurements in the clothing size designation provides a flexible combination of chest, waist and hip measurements for women and chest and waist measurements for men. Consideration of the range of additional control vertical measurements is confirmed by calculating the indices of balance in the longitudinal direction of the female figure of size 164-96-104.

Modern Clothing Design Based on Human 3D Somatosensory Technology

In the era of e-commerce, online clothing sales have grown rapidly, but the differences in clothing size and style details have brought serious problems to consumers’ purchase. At present, as far as online clothing sales are concerned, the most prominent problem is that consumers cannot try on clothes online as they do in the real environment, let alone touch and feel the texture of clothing fabrics. This has seriously affected and restricted the development of online clothing sales. The after-sales service cannot keep up, which is mainly reflected in the consumer return and replacement which is not convenient, and the purchased clothing cannot be received in time and other aspects. 3D garment customization is a hot area of research at present, and there is a wide range of needs to implement a software that can be customized for users. As a depth sensing technology, it strengthens the ability of video sensors for target recognition and brings new advances in sensing and processing deep vision tasks. Aiming at this problem, this paper proposes a modern clothing design scheme based on human 3D somatosensory technology. The designed scheme focuses on the acquisition of human size data and the personalized combination process of clothing components in the process of clothing design. In the scene where the human body changes greatly or the human body moves rapidly, the real-time limb coverage of the clothing fabric is higher, and the posture matching degree is higher. Applicable clothing types and human body pose types are more abundant, with a higher sense of reality. The clothing perception model fused with profile and girth features can match the dimensional changes of key parts of the human body. Through personalized clothing design, the combination of clothing parts is used to provide more choices of clothing styles, colors, and sizes. Using this solution, the time of clothing design can be greatly shortened, and the user’s satisfaction with the clothing design can be improved.

Outdoor Clothing Design for Traffic Safety Based on Big Data and Artificial Intelligence

With the development of technologies in various fields, more and more technologies have been applied to safety clothing, which has led to the rapid development of safety clothing. The improvement of living standards is accompanied by the change of consumption concepts. Consumers’ requirements for clothing products have become more artistic, healthier, and more ecological, and they look forward to more and better safety clothing to meet their health needs. In this context, this article studies traffic safety outdoor clothing design based on big data (BD) and AI. This article introduces the design method of outdoor safety clothing for traffic based on BD and machine learning in artificial intelligence (AI) and did two experiments. To this end, this paper adopts a Deep Belief Network (DBN), which is trained layer by layer through Restricted Boltzmann Machine (RBM), and successfully solves the problems of lack of a large number of labeled samples and easy to fall into local optimum. The first experiment is to test the accuracy of various machine learning algorithms for clothing size measurement. The results obtained are as follows: the predicted value of the DBN neural network is the closest to the actual value, the average prediction accuracy of DBN for the cuff size is 90%, and the average prediction accuracy for the neck circumference is 91.5%. The second experiment is to investigate the functional needs and performance concerns of children and outdoor workers. The results of the experiment are as follows: for children, 79.9% of people want clothing to have a positioning function, which accounts for the highest proportion. For outdoor workers, the most important clothing function they need is eye-catching style, and 90.1% of those choose this option. In terms of clothing performance concerns, most people choose to care very much, and the second most people choose to care about comfort.

A Study on the Size of Kids Wear

Are parents purchasing larger-sized clothing for their children? Kids are increasingly considered individual consumers by the market. Parents, on the other hand, are ignoring this and purchasing larger-sized clothing for their children. This research includes a look at the size of children's clothing. For children, there is a separate styling section. A survey was also done to learn about their attitudes toward their children's shopping. To combat this, a system has been devised that encourages parents to purchase correct sizes for their children. To achieve the purpose, this work used trimmings and fabric manipulation.

Construction of Knowledge Graph of 3D Clothing Design Resources Based on Multimodal Clustering Network.

The construction of 3D design model is a hotspot of applied research in the fields of clothing functional design system teaching and display. The simple 3D clothing visualization postprocessing lacks interactive functions, which is a hot issue that needs to be solved urgently at present. Based on analyzing the existing clothing modeling technology, template technology, and fusion technology, and based on the multimodal clustering network theory, this paper proposes a 3D clothing design resource knowledge graph modeling method with multiple fusion of features and templates. The position of each joint point is converted into the coordinate system centered on the torso point in advance and normalized to avoid the problem that the relative position of the camera and the collector cannot be determined, and the shape of different collectors is different. The paper provides a multimodal clustering network intelligence method, illustrates the interoperability of users switching between different design networks in the seamless connection movement, and combines the hybrid intelligence algorithm with the fuzzy logic interpretation algorithm to solve the problems in the field of 3D clothing design service quality. During the simulation process, the research scheme builds a logical multimodal clustering network framework, which integrates compatibility access and global access partition fusion of style templates to achieve information extraction of clothing parts. The experimental results show that the realistic 3D clothing modeling can be achieved by layering the 3D clothing map, contour features, clothing size features, and color texture features with the modeling template. The developed ActiveX control is mounted on MSN, and the system is compatible. The performance and integration rate reached 77.1% and 89.7%, respectively, which effectively strengthened the practical role of the 3D clothing design system.

Automatic Measurements of Garment Sizes Using Computer Vision Deep Learning Models and Point Cloud Data

Automatic garment size measurement approaches using computer vision algorithms have been attempted in various ways, but there are still many limitations to overcome. One limitation is that the process involves 2D images, which results in constraints in the process of determining the actual distance between the estimated points. To solve this problem, in this paper, we propose an automated method for measuring garment sizes using computer vision deep learning models and point cloud data. In the proposed method, a deep learning-based keypoint estimation model is first used to capture the clothing size measurement points from 2D images. Then, point cloud data from a LiDAR sensor are used to provide real-world distance information to calculate the actual clothing sizes. As the proposed method uses a mobile device equipped with a LiDAR sensor and camera, it is also more easily configurable than extant methods, which have varied constraints. Experimental results show that our method is not only precise but also robust in measuring the size regardless of the shape, direction, or design of the clothes in two different environments, with 1.59% and 2.08% of the average relative error, respectively.

An improved clothing size recommendation approach based on subdivision of female body types

ABSTRACT With the rapid development of clothing online shopping, customers have a higher demand for more accurate clothing size recommendation systems. The work this paper presented is compatible with the four body types specified in the Chinese standard. Then, the subdivision was performed where each of the major body types corresponded to four characteristic indices. In addition, each characteristic index had three attributes. Therefore, the female body can be subdivided into 81 types for each major type. Subsequently, this paper suggests an improved clothing size recommendation system that considers subdivided body types. The recommendation criteria in the approach were refined by a pressure distribution analysis through virtual fitting simulation. Finally, case studies were conducted on customers of body type A (accounting for 52.73% of bodies) to demonstrate the procedures of the proposed approach.

Ecommerce Assisted by Machine Learning

Abstract: E-commerce applications are growing and becoming complex day by day. Every new feature is taking the time of the user and reducing the speed or the process of purchasing anything. Consumers demand comfortability, usability, authenticity on E-commerce websites, and hence to provide with these needs Machine learning should be introduced in the E-commerce website. In this paper, we will learn various methods to help boost E-commerce using Machine Learning. Product Recommendation will severely help reduce the time of users on purchasing a product as it recommends products based on history. Cloth size estimation can be used for taking measurements for helping users purchase clothes with accurate measurements. Dynamic Pricing can be used to generate discounts and prices based on user history for maintaining users. Fake Review Prediction can be used to filter the reviews for better business. A chatbot can be used to help users during any stage of purchase or after the purchase as support. All of this can contribute towards the personalized experience of consumers and will be discussed in this paper. Keywords: Ecommerce, Machine Learning, Dynamic pricing, Chatbot, Product Recommendation, Cloth Size Estimation, Fake Review Prediction, Ecommerce

Fashionable sportswear working as a body measurement collecting tool

PurposeA two-dimensional (2D) body measurement system was implemented to study the application of sportswear design in measurement garment development. A total of 50 participants were recruited. The basic demographic information and sportswear preference data were collected through a survey to understand consumer preferences and acceptance of the new designs. The body measurements were collected through both the selected 2D measurement system and a commercial three-dimensional scanning booth to evaluate measurement accuracy.Design/methodology/approachFinding the right size has been a long-existing problem for clothing consumers. Size problem is the most common reason for e-commerce returns and adds a high cost for retailers. One possible solution is to offer consumers an easy-to-use method that extracts accurate body measurements to be used for clothing size selection. The purpose of this research is to apply sportswear design elements on measurement garments to see if consumers’ interest in using the 2D measurement system can be increased without influencing the measurement accuracy.FindingsThe results showed that the added design features increased consumers’ interest in using 2D body measuring technology without significantly influencing measurement accuracy.Originality/valueThis research applied sportswear elements to convert a 2D measurement bodysuit to a fashionable clothing product. The solution resolved users’ privacy concerns and increased their acceptance and use of the technology. Other studies have not focused on using aesthetic features to improve the 2D measurement technology.

Body size in early life and the risk of postmenopausal breast cancer

BackgroundGreater early life adiposity has been reported to reduce postmenopausal breast cancer risk but it is unclear whether this association varies by tumour characteristics. We aimed to assess associations of early life body size with postmenopausal breast cancer and its subtypes, allowing for body size at other ages.MethodsA total of 342,079 postmenopausal UK women who reported their body size at age 10, clothes size at age 20, and body mass index (BMI) at baseline (around age 60) were followed by record linkage to national databases for cancers and deaths. Cox regression yielded adjusted relative risks (RRs) of breast cancer, overall and by tumour subtype, in relation to body size at different ages.ResultsDuring an average follow-up of 14 years, 15,506 breast cancers were diagnosed. After adjustment for 15 potential confounders, greater BMI at age 60 was associated with an increased risk of postmenopausal breast cancer (RR per 5 kg/m2=1.20, 95%CI 1.18-1.22) whereas greater adiposity in childhood and, to a lesser extent, early adulthood, was associated with a reduced risk (0.70, 0.66-0.74, and 0.92, 0.89-0.96, respectively). Additional adjustment for midlife BMI strengthened associations with BMI at both age 10 (0.63, 0.60-0.68) and at age 20 (0.78, 0.75-0.81). The association with midlife adiposity was confined to hormone sensitive subtypes but early life adiposity had a similar impact on the risk of all subtypes.ConclusionEarly life and midlife adiposity have opposite effects on postmenopausal breast cancer risk and the biological mechanisms underlying these associations are likely to differ.

Creating predictive clothing size models for online customers

A disadvantage to online clothes shopping is the inability to try on clothing to test the fit. A class project is discussed where students consult with the CEO of an online mensware clothing company to explore ways in which an online clothing customer can be assured of a superior fit by developing statistical models based on a shopper’s height and weight to predict measurements needed to create a suit that feels custom-made. The dataset is most amenable to use with students who have previously been exposed to simple linear regression, and can be used to explore multiple regression topics such as interaction terms, influential points, transformations, and polynomial predictors. Discussion points are included for more advanced topics such as canonical correlation, clustering, and dimension reduction.

Energy Balance-Related Factors and Risk of Colorectal Cancer Expressing Different Levels of Proteins Involved in the Warburg Effect.

Energy balance-related factors [body mass index (BMI), waist circumference, physical activity] have been associated with colorectal cancer risk. Warburg effect activation via PI3K/Akt signaling is one of the proposed mechanisms. We investigated whether energy balance-related factors were associated with risk of Warburg subtypes in colorectal cancer. We investigated this using immunohistochemistry for six proteins involved in the Warburg effect (LDHA, GLUT1, MCT4, PKM2, P53, PTEN) on tissue microarrays of 2,399 incident colorectal cancer cases from the prospective Netherlands Cohort Study (ntotal = 120,852; nsubcohort = 5,000; aged 55-69 in 1986; 20.3 years follow-up). Data analyses included 3,911 subcohort members and 1,972 colorectal cancer cases with complete covariate data. Expression levels of all proteins were combined into a pathway-based sum score and categorized into three "Warburg subtypes" (Warburg-low/moderate/high). Multivariable Cox regression analyses were used to estimate associations of BMI, clothing size (waist circumference proxy), and physical activity with Warburg subtypes in colorectal cancer. BMI and clothing size were positively associated with Warburg-moderate and Warburg-high colon cancer risk in men (Pheterogeneity = 0.192). In women, clothing size was positively associated with Warburg-low and Warburg-high colon cancer (Pheterogeneity = 0.005). Nonoccupational physical activity was inversely associated with Warburg-low and Warburg-moderate colon cancer in women (Pheterogeneity = 0.045), but positively associated with Warburg-high rectal cancer in men (Pheterogeneity = 0.089). The Warburg effect might be involved in associations between adiposity and colon cancer risk, though additional mechanisms could be at play in women as well. The inverse association between physical activity and colon cancer might be explained by mechanisms other than the Warburg effect. Further research is needed to reproduce these results and investigate possible additional mechanisms.