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With the use of 3D body scanners, body measurement techniques can be non‐contact, instant, and accurate. However, how each scanner establishes landmarks and takes the measurements should be established so that standardization of the data capture can be realized. The purpose of this study was to compare body‐scanning measurement extraction methods and terminology with traditional anthropometric methods. A total of 21 measurements were chosen as being critical to the design of well‐fitting garments. Current body scanners were analyzed for availability of information, willingness of company cooperation, and relevance to applications in the apparel industry. On each of the 21 measurements, standard measurement procedure was identified for three different scanners: [TC]², Cyberware, and SYMCAD. Of the 21 measures in the study, [TC]² was the scanner that had the most measures identified for the study and also had the capability of producing many more with specific application for apparel.

The purpose of this paper is to validate the accuracy of point cloud data generated from a 3D body scanner.

A female dress form was scanned with an X‐ray computed tomography (CT) system and a 3D body scanning system. The point cloud data from four axial slices of the body scan (BS) data were compared with the corresponding axial slices from the CT data. Length and cross‐sectional area measurements of each slice were computed for each scanning technique.

The point cloud data from the body scanner were accurate to at least 2.0 percent when compared with the CT data. In many cases, the length and area measurements from the two types of scans varied by less than 1.0 percent.

Only two length measurements and a cross‐sectional area measurement were compared for each axial slice, resulting in a good first attempt of validation of the BS data. Additional methods of comparison should be employed for complete validation of the data. The dress form was scanned only once with each scanning device, so little can be said about the repeatability of the results.

Accuracy of the point cloud data from the 3D body scanner indicates that the main issues for the use of body scanners as anthropometric measurement tools are those of standardization, feature locations, and positioning of the subject.

Comparisons of point cloud data from a 3D body scanner with CT data had not previously been performed, and these results indicate that the point cloud data are accurate to at least 2.0 percent.

The purpose of this paper is to develop an automated human body measurement extraction system using simple inexpensive equipment with minimum requirement of human assistance. This research further leads to the comparison of extracted measurements to established methods to analyze the error. The extracted measurements can be used to assist the production of custom-fit apparel. This is an effort to reduce the cost of expensive 3-D body scanners and to make the system available to the user at home.

A single camera body measurement system is proposed, implemented, and pilot tested. This system involves a personal computer and a webcam operating within a space of controlled lighting. The system will take two images of the user, extract body silhouettes, and perform measurement extraction. The camera is automatically calibrated using the software each time of scanning considering the scanning space. The user will select a front view and a side view among the images captured, and specify the height. In this pilot study, 31 subjects were recruited and the accuracy of 8 human body measurements were compared with the manual measurements and measurements extracted from a commercial 3-D body scanner.

The system achieved reasonable measurement performance within 10 percent accuracy for seven out of the eight measurements, while four out of eight parameters obtained a performance similar to the commercial scanner. It is proved that human body measurement extraction can be done using inexpensive equipment to obtain reasonable results.

This study is aimed at developing a proof-of-concept for inexpensive body scanning system, with an effort to benchmark measurement accuracy, available to an average user providing the ability to acquire self-body measurements to be used to purchase custom-fit apparel. This system can potentially boost the customization of apparel and revolutionize online shopping of custom-fit apparel.

Aims to check the validity of measurements of dynamic postures recorded by a body scanner.

Measurements between various anatomical landmarks have been taken both manually and using a 3D body scanner so that the validity of the measurements might be assessed when dynamic postures are adopted. Mechanical measurements of changes in the body surface dimensions have been compared with figures produced by a body scanner for both the standard natural position and for five dynamic postures, which must be accommodated when designing high‐performance garments.

Although the 3D body scanner collects data almost instantaneously and without physical contact with the target surface, the readings taken in respect of dynamic poses showed significant variations from manually‐taken measurements, with discrepancies as large as 6.8 cm over a 16 cm distance.

The research has only been carried out on a very limited number of subjects. However, significant differences between manual and automatic body measurements are clearly demonstrated.

The research showed that as there are as yet no universally‐accepted conventions for 3D scanner measurements, the results appear to be optimised for the natural anatomical position. Body‐scanners are not well‐suited to taking measurements of dynamic postures expected in sporting activities.

Measurements of anthropometric landmarks for high‐performance activities have not previously been assessed, and these results usefully indicate the limitations of current 3D scanning technology.

Presents a standard data format for describing and interpolating 3‐D human body shapes from data collected by a 3‐D body scanner. The body data were treated as a series of horizontal cross‐sections. Each cross‐section was described by 16 data points. The 3‐D surface can be calculated by interpolating between these sections. This procedure allowed editing and manipulation of raw scanned data, as well as substantial data reduction. Horizontal cross‐sections of the body were chosen to correspond to particular anatomical surface landmarks, rather than distances from a reference point. Hence, each data element described a particular anatomical location, irrespective of body shape and size. This feature allowed comparison and averaging of 3‐D shapes, greatly enhancing the application of 3‐D scanned data. The standard data format allows 3‐D scanned data to be transferred into CAD/CAM systems for automated garment design and manikin manufacture.

Identification of human body shapes has been a key issue to develop sizing standards for ready‐to‐wear and to develop made‐to‐measure applications. Current methods to identify the body shapes are mostly based on subjective/visual determination approaches. The purpose of this paper is to look for numerical evaluation parameters for an objective method in order to classify the body shapes and to build up an automated process link.

Female subjects were chosen for the experimental design. 3D body scanning technology was integrated in the process for measurement taking and body silhouette detection of the sample group. Based on this sample data set, body shape identification was realized by referees as visual analyses, and additionally, an objective method was tried out by using body dimensions as numerical evaluation parameters. Obtained results with the sample group were inserted in a database and a body shape calculation tool was developed.

Statistical analyses showed that there is mostly a good agreement between the pairs of the evaluations including the objective calculation methods and the subjective assessments of the referees. The calculation tool was designed as web‐based software in order to integrate with further developments and automation purposes.

A new automatic tool was developed to make the body shape classification objective and repeatable. By integrating this tool to the product development chain, a continuous process link can be provided for the companies through the way for better fitting clothing.

The purpose of this study is to evaluate fit of the basic garments made for Taiwanese female students with various figure characteristics. The basic garments are produced according to patterns derived from the PDS 2000 and APDS‐3D systems.

This study recruited ten Taiwanese female subjects who represented various figure characteristics. After scanning each subject, the body measurements with additional functional ease were manually entered into the APDS‐3D system accompanied with the PDS 2000 system to generate the block patterns. These patterns were used to make basic garments worn on the subjects for fit evaluations. T‐test and one‐way ANOVA were employed to investigate if any statistically significant differences between figure characteristics of subjects exist.

After statistical analysis, results showed that the percentage of tolerance allowed by the system in preventing incorrect measurements has to be revised and more measurements have to be included into the APDS‐3D system. Furthermore, female students who exhibit multiple figure variations complicate fitting problems. For example, sloped‐shoulder subjects with narrow shoulders and forward stance generate the problem of extra fabric gathering at the shoulder tips as well as looseness at the upper chest. Therefore, figure variations have to be analyzed in a future study.

The convenient sample with limited size does not allow generalization of figure variations associated with fit problems in all colleges or universities located in Taiwan.

Few researchers have analyzed fit problems on garments made for females with figure variations, but none of them use 3D body scanners in combination with computer‐aided design systems to test fit on basic garments for females with various physical characteristics.

To make individualized men's basic upper garment patterns without sleeves, the authors developed a measuring garment that measures necessary body dimensions and angles all together. Additionally, the authors proposed a method for making individualized basic body block patterns using the obtained dimensions and angles.

The authors decided on the locations of the dimensions of the body required for making the individualized garments. The authors then sewed multiple stretchable capacitance sensors to corresponding locations on a stretchable T-shirt. To obtain the dimensions with sensors of short length, the authors indirectly obtained each length from the relationship between the actual body length and the capacitance of the sensor. Beforehand, the authors obtained linear-approximation equations for the relationship between actual body dimensions and the capacitance of sensors for five participants and a dummy. The authors then used the measuring garment and the equations to obtain the body dimensions of another six participants. The authors compared the obtained and actual body dimensions to verify the equations. The authors made individualized upper-garment patterns without sleeves and garments for the 11 participants with the obtained dimensions and angles. The authors verified the proposed method in wearing tests comparing garments designed using the proposed method with conventionally designed garments.

Using the measuring garment, the authors obtained body dimensions close to actual body dimensions. A pattern of the individualized basic upper garment using the obtained dimensions and angles could be drawn. Compared with the conventional patterns, the individualized patterns had notable differences in the locations of the shoulder point and side neck point and directions of the shoulder line, which relate to the shoulder shape (i.e. square, sloping, forward, or backward). In wearing tests, all participants declared that the individualized garment better fitted their shoulders than the conventional garment without tightness around the shoulders, neck, and armpits. The proposed method with the developed measuring garment was thus found to be effective in designing individualized garments.

This paper presents the possibility of not only measuring body dimensions but also designing individualized basic upper garments using the proposed measuring garment. The proposed measuring garment will assist the efficient manufacture of individualized upper garments without a three-dimensional scanner or special skills.

The purpose of this paper is to determine the effects of clothing ease and body postures on the size and distribution of the air gap as well as the body coverage with the clothing.

Visual and quantitative analyses were conducted using a 3D body scanner and Geomagic Software. The air gap size and clothing area factor (f_cl) in three test coverall and seven selected postures were calculated and compared.

The results indicated that both the clothing ease and body postures had a strong effect on the air gap and clothing coverage, especially the more complex the postures, the wider the range of influence. Nevertheless, these effects varied over body regions, being stronger at the lower body than the upper body. The air gap size at the left side of the body was generally larger than the right side. It was also found that the clothing coverage was linearly correlated with the air gap size and could be employed as an indicator to evaluate clothing protective capabilities.

The findings suggested that greater attention should be paid to the protection and flexibility at the lower body and asymmetrical distribution of the air gap should be considered in the future air gap modeling.

The outcomes provided useful information to improve the protective clothing and develop more realistic air gap models to simulate the heat and mass transfer.

The purpose of this paper is to propose a method to automatically generate individualized body size measurements from cloud point of a body scanner. It aims to propose a fast, reliable, and unambiguous method to obtain human body measurements for use in the garment industry.

Based on a previous study by the authors, geometric features on the scanned body are identified by computerized algorithms through mathematical definitions. Feature lines situated on the human body surface are created as polylines that pass through the body's features and three types of computer measurements (tape‐measurement, contour‐measurement, and linear‐measurement) are provided.

By dividing the body surface into rectangular patches using the feature lines as boundaries, the body can be reconstructed easily with a minimal amount of triangles while retaining the essential shape. The proposed measuring method applies to most manual measurements used in the garment industry. The authors evaluated the anthropometry variations of the same subject to explore the reliability of the proposed method. It was found that the precision of the method is well below the standard requirement of the traditional manual method.

In this research, subjects were scanned in standing pose; this pose minimizes regions obstructed by body parts and permits maximal acquisition of as many key landmarks. Since the features are identified by geometric analysis without the need for marker attachment, measurements of the required sitting position are impossible to obtain in the current study.

Resolution of meshing can be changed according to application requirements. Contrary to the traditional manual method, efficiency and precision are the advantages of the present method.