An intelligent recommendation system for personalised parametric garment patterns by integrating designer's knowledge and 3D body measurements.

Garment pattern-making is one of the most important parts of the apparel industry. However, traditional pattern-making is an experience-based work, very time-consuming and ignores the body shape difference. This paper proposes a parametric design method for garment pattern based on body dimensions acquired from a body scanner and body features (body feature points and three segmented body part shape classification) identified by designers according to their professional knowledge. By using this method, we construct a men's shirt pattern recommendation system oriented to personalised fit. The system consists of two databases and three models. The two databases include a relational database (Database I) and a personalised basic pattern (PBP) database (Database II). The Database I is based on manual and three-dimensional (3D) measurements of human bodies by using designer's knowledge. And Database I is a relational database, which is organised in terms of the relational model of the body part shape and its key body feature dimensions. After a deep analysis of measured data, the irrelevant measured dimensions to human body shape have been excluded by designers and extract representative human body feature dimensions. In addition, the relations between body shapes and previously identified body feature dimensions have been modelled. From the above relational model, we label key feature point positions on the corresponding 3D body model obtained from 3D body scanning and correct the whole 3D human upper body model into the semantically interpretable one. The 3D personalised basic pattern is drawn on the corrected model based on these key feature points. By using three-dimensional to two-dimensional (3D-to-2D) flattening technology, a 2D flatten graph of the 3D personalised basic pattern of the interpretable model is obtained and slightly adjusted to the form suitable for industrial production, i.e., PBP and the PBP database (Database II) is built. In addition, the three models include a basic pattern parametric model (Model I) (characterizing the relations between the basic pattern and its key influencing human dimensions (chest girth and back length)), a regression model (Model II) which enables to infer from basic pattern to PBP for three body parts based on the one-to-one correspondence of key points between the PBPs and the basic patterns and a personalised shirt pattern parametric model (Model III) (characterizing the structural relations between the personalised shirt pattern (PBPshirt) and PBP). The initial input items of the recommendation system are the body dimension constraint parameters, including chest girth, back length and the body feature dimensions used to determine each body part shape as well as three shirt style constraint parameters (slim, regular and loose). By using Model I, the corresponding basic pattern can be generated through the user's chest girth and back length. Body feature dimensions determine the three body parts' shapes. Then, Model II is used to generate the PBP for the corresponding body parts shape. Based on the shirt style chosen by the user, Mode III is used to generate the PBPshirt from the PBP. The output of the recommendation system is a fit-oriented PBPshirt. Moreover, if the PBPshirt is unsatisfactory after a virtual try-on, four adjustable parameters (front side-seam dart, back side-seam dart, waist dart and garment bodice length) are designed to adjust the PBPshirt generated by the proposed recommendation system.

The proposed recommendation system combines the designer's knowledge of manual measurement of the human body, traditional 2D pattern-making methods and 3D-to-2D flattening technology to generate personalised shirt patterns automatically and quickly, thus significantly improving pattern-making efficiency. The reliance on designers in the garment production process is reduced. Even users with no pattern-making knowledge can also develop professional shirt patterns by using our proposed system.

A study on segmentation and refinement of key human body parts by integrating manual measurements.

Optimal ergonomic design for consumer goods (such as garments and furniture) cannot be perfectly realised because of imprecise interactions between products and human models. In this paper, we propose a new body classification method that integrates human skeleton features, expert experience, manual measurement methods, and statistical analysis (principal component analysis and K-means clustering). Taking the upper body of young males as an example, the proposed method enables the classification of upper bodies into a number of levels at three key body segments (the arm root [seven levels], the shoulder [five levels], and the torso [below the shoulder, eight levels]). From several experiments, we found that the proposed method can lead to more accurate results than the classical classification methods based on three-dimensional (3 D) human model and can provide semantic knowledge of human body shapes. This includes interpretations of the classification results at these three body segments and key feature point positions, as determined by skeleton features and expert experience. Quantitative analysis also demonstrates that the reconstruction errors satisfy the requirements of garment design and production. Practitioner summary The acquisition and classification of anthropometric data constitute the basis of ergonomic design. This paper presents a new method for body classification that leads to more accurate results than classical classification methods (which are based on human body models). We also provide semantic knowledge about the shape of human body. The proposed method can also be extended to 3 D body modelling and to the design of other consumer products, such as furniture, seats, and cars. Abbreviations: PCA: principal component analysis; KMO: Kaiser-Meyer-Olkin; ANOVA: analysis of variance; 3D: three-dimensional; 2D: two-dimensional; ISO: International Standardisation Organisation; BFB: body-feature-based.

Modeling and Simulation of Human Body Heat Transfer System Based on Air Space Values in 3D Clothing Model.

Comfort can be considered as subjective feeling, which could be affected by the external ambient, by the physical activity, and by clothing. Considering the human body heat transfer system, it mainly depends on various parameters including clothing materials, external and internal environment, etc. The purpose of the current paper is to study and establish a quantitative relationship between one of the clothing parameters, ease allowance (air gap values) and the heat transfer through the human body to clothing materials and then to the environment. The study considered clothing which is integrated with the 3D ease allowance from the anthropometric and morphological data. Such incorporating of the clothing's 3D ease control was essential to properly manage the air space between the body and the proposed clothing thermal regulation model. In the context of thermal comfort, a clothing system consisting of the human body, an ease allowance under clothing, a layer of textile materials, and a peripheral layer adjacent to the textile material was used. For the complete system, the heat transfer from the skin to the environment, which is influenced by thermoregulation of the human body, air gap, tissue, and environmental conditions were also considered. To model and predict the heat transfer between the human body and the temperature of skin and clothes, a 3D adaptive garment which could be adjusted with ease allowance was used. In the paper, a thermoregulatory model was developed and proposed to predict the temperature and heat within clothing material, skin, and air space. Based on the result, in general the main difference in the temperature of clothing and skin from segment to segment is due to the uneven distribution of air layers under the clothing.

Spectral clustering applied for dynamic contrast-enhanced MR analysis of time-intensity curves.

Dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) represents an emerging method for the prediction of biomarker responses in cancer. However, DCE images remain difficult to analyze and interpret. Although pharmacokinetic approaches, which involve multi-step processes, can provide a general framework for the interpretation of these data, they are still too complex for robust and accurate implementation. Therefore, statistical data analysis techniques were recently suggested as another valid interpretation strategy for DCE-MRI. In this context, we propose a spectral clustering approach for the analysis of DCE-MRI time-intensity signals. This graph theory-based method allows for the grouping of signals after spatial transformation. Subsequently, these data clusters can be labeled following comparison to arterial signals. Here, we have performed experiments with simulated (i.e., generated via pharmacokinetic modeling) and clinical (i.e., obtained from patients scanned during prostate cancer diagnosis) data sets in order to demonstrate the feasibility and applicability of this kind of unsupervised and non-parametric approach.