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Automated anthropometric data collection using 3D whole body scanners
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Expert Systems with Applications Expert Systems with Applications 35 (2008) 407–414 www.elsevier.com/locate/eswa
Automated anthropometric data collection using 3D whole body scanners Jun-Ming Lu, Mao-Jiun J. Wang
*
Department of Industrial Engineering and Engineering Management, National Tsing Hua University 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan, ROC
Abstract The development of three-dimensional (3D) whole body scanner opens opportunities for measuring human body more efficiently. While taking measurements from 3D scanning data, markers are usually placed on human body to facilitate landmarking and data collection. But the procedure of placing markers is very tedious and may involve human errors. The objective of this study is to develop an automated anthropometric data collection system to eliminate manual intervention. For 3D image analysis, the first step is to segment the body parts by analyzing the 2D silhouette. Then we use the approximate location estimates as the references for performing initial searches of the landmarks. Subsequently, four algorithms including silhouette analysis, minimum circumference determination, grayscale detection, and human-body contour plots are developed to extract 12 landmarks and three characteristic lines on human body. Finally, from the results of automated landmarking, 104 anthropometric data can be obtained. To evaluate the validity and reliability of the automated anthropometric data collection system, 189 human subjects were scanned and the scanning data were processed by the system. The results demonstrated that the system was very effective and robust. Based on the newly developed system, many applications can be extended. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Three-dimensional (3D) whole body scanner; Anthropometry; Landmarking
1. Introduction
1.1. 3D scanning technology for measurements
With the growing trend of globalization, the concept of mass customization in product design is becoming an important issue. For designers, measurements of human body are necessary for the development of well-fitted products, such as clothing and shoes. Traditionally, human-body dimensions are measured manually by using measuring tapes and calipers. However, the procedure is time-consuming and involving direct contact with human subjects. With the advancement of photonics technology, it is now possible to make fast and contact-free measurements by using 3D whole body scanners.
The Loughborough Anthropometric Shadow Scanner (LASS) was one of the first 3D scanners being used for collecting 3D size and shape information of human body (Jones, West, Harris, & Read, 1989). Later on, Research Institute of Human Engineering for Quality of Life (HQL) in Japan conducted a large-scale anthropometric survey with both traditional methods and whole body scanners (Research Institute of Human Engineering for Quality Life (HQL)). In addition, a multinational project, called CAESAR (Civilian American and European Surface Anthropometry Resource), was undertaken from 1998 to 2002 in the US, the Netherlands and Italy (Daanen & van de Water, 1998). They used 3D whole body scanners to gather anthropometric data and construct a database of 3D human models. More recently, a growing number of countries endeavored to use 3D scanners to
*
Corresponding author. Tel.: +886 3 5742655; fax: +886 3 5737107. E-mail address: [email protected] (M.-J.J. Wang).
0957-4174/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2007.07.008
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conduct national surveys, including China, Korea, Taiwan, UK, US and France (Campagne Nationale de Mensuration, 2003–2004; Fan, Yu, & Hunter, 2004; Treleaven et al., 2004). 1.2. Preparing 3D scanning data In order to make accurate measurements, it is critical to ensure the quality of the scanning images. Since the 3D whole body scanner is an optical measurement equipment, it is very sensitive to the lighting condition and the geometric nature of the scanning object. Thus, strict lighting control and the use of measurement attire for scanning can help to enhance the quality of scanning images (D’Apuzzo, 2005; Min, Nam, & Choi, 2003). Besides, movement artifact is another important factor affecting the quality of scanning images. Asking the subject to adopt standard posture and to hold breath while scanning can help to minimize the effect of body movement (Daanen, Brunsman, & Robinette, 1997). For a commonly used 3D whole body scanner, it can capture more than 300,000 scattered points from the surface of human body. By organizing and analyzing the 3D scanning data, the 3D human model can be constructed, and many applications can be further extended. 1.3. Segmentation of 3D scanning data For the purpose of landmarking and body dimension collection, a 3D human model is usually segmented at armpits and crotch. Five body parts including head and torso, both arms, and both legs can be identified. For body parts segmentation, Nurre, Connor, Lewark, and Collier (2000) proposed a cusp algorithm for segmenting the 3D scanning data of a human body. For each scan, the arms and legs were located at the Scy point and the crotch, respectively. Further, Wang, Chang, and Yuen (2003) applied fuzzy logic concept to locate the armholes and crotch, and then to separate the arms and legs from the trunk. After segmentation, the scanning data will be ready for landmarking.
process and may involve human errors. Therefore, developing marker-free techniques for landmarking becomes an important issue for analyzing the 3D whole body scanning data. For the method of automated landmarking, analyzing the geometry of human body such as the silhouette is a logical approach (Buxton et al., 2000). In addition, researchers also used the method of reconstructing curves and surfaces to locate the landmarks (Douros, Dekker, & Buxton, 1999). But the procedure of curve and surface fitting is complex and time-consuming, and the fitted data may not be able to reserve the geometry of human body exactly. Moreover, a template mapping approach, which makes use of the information from the database of 3D human models, makes it possible to identify landmarks efficiently (Allen, Curless, & Popovic´, 2003). But this technique requires a comprehensive database of 3D human models. 1.5. Measuring the human body Based on the landmarks extracted, measurements can be made by placing a virtual measuring tape or caliper on the surface of human body. Even though the 3D scanner reduces the time and cost for measuring the human body, the accuracy and reliability of the measurements still need to be verified. Brooke-Wavell, Jones, and West (1994) compared the measurements obtained by using 3D whole body scanner and the traditional anthropometry method and found similar results. But traditional anthropometry showed a better repeatability. Moreover, although the repeatability of the scanner-based methods was no better than that of the traditional anthropometry, it could meet the tailors’ acceptance criteria (Bradtmiller & Gross, 1999). Therefore, the use of 3D body scanners for measuring human dimensions can be practical for some applications, such as garment making in the apparel industry. Nevertheless, in order to extend its use as a substitute for traditional anthropometry, improvements are needed. 1.6. Scope of this study
1.4. Landmarking on 3D scanning data Landmarking is to find out the anatomical landmarks that are used to define body size and shape. Since landmarks are mostly bony protrusions, palpation works are needed for identification. In general, anthropologists place markers or stickers on the surface of human body to highlight the positions. Thus the positions of the landmarks can be easily identified on the scanning image with human eyes. Further, by using the color information obtained from the CCD cameras in the scanning heads, the landmarks can also be identified by analyzing the RGB information in the scanning image (Burnsides, Boehmer, & Robinette, 2001; Wang, Wu, Lin, Yang, & Lu, 2007). However, the procedure of placing markers on body surface is a tedious
The objective of this study is to develop a system for automated anthropometric data collection by using the 3D whole body scanner. Prior to data analysis, body segmentation and initial searches are performed with the scanning data. This is given in Section 2. Then, in Section 3, four algorithms are proposed for automated landmarking, including silhouette analysis, minimum circumference determination, gray-scale detection, and human-body contour plots. Further, in Section 4, the detected landmarks enable anthropometric data collection by using the approximation methods. Subsequently, in Section 5, experiments are conducted to validate the system. Finally, the results and important findings are highlighted and concluded in Section 6.
J.-M. Lu, M.-J.J. Wang / Expert Systems with Applications 35 (2008) 407–414
2. Data preparation 2.1. Scanning The 3D scanner used in this study is the Vitus 3-D 1600 whole body scanning system, as shown in Fig. 1. The surface of human body can be captured by four sets of laser beams and CCD cameras in 20 s. In order to obtain optimal scanning image, the subject is asked to adopt a specified posture with standard measurement attire and cap. Arms are kept apart from the torso to facilitate body segmentation. In addition, to minimize the effect of movements, subjects are requested to hold their breaths while scanning. If there is any problem with the posture or if the subject moves, the scanning data will be considered invalid. The number of data points captured varies from 35,000 to 65,000, depending on the volume of the human body. With the CCD cameras, the color information of the human body can be recorded as well. Prior to data analysis, noise has to be removed. The most intuitive method for noise reduction is to compare the distance between any two points with a given threshold. If the distance does not exceed the threshold, they should be considered as neighboring points. Any point without neighboring points is considered as noise and has to be eliminated. 2.2. Segmentation In order to simplify the process of landmarking, the whole body scanning data has to be segmented into five parts, including head and torso, left arm, right arm, left leg, and right leg. Since the head and torso, left arm,
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and right arm are connected at the armpits, it is necessary to locate the armpits before separating these three parts. Accordingly, the crotch, where the torso and two legs meet, is another key location for segmentation. By projecting the 3D scanning image onto the coronal plane, the silhouette of the human body can be obtained. Subsequently, the armpits and crotch can be found at the local maximum points on the silhouette of the scanning image. Then the body parts are segmented along the horizontal planes that pass through the armpits and crotch. 2.3. Initial searches After segmentation, each body part remains to contain more than 10,000 points. Thus, initial searches with approximate location estimates are necessary. The purpose of initial search is to locate the possible positions based on the characteristics of each subject, such as gender and age. The estimates adopted are derived from a comprehensive anthropometric database constructed in 2000 (Wang, Wang, & Lin, 2000). For both males and females, there are 12 vertical location estimates (in proportion to the stature) and two horizontal location estimates (in proportion to the length of a body segment) being utilized, as presented in Table 1. Once the subject’s gender is given, the estimated position of a certain landmark can be applied to locate the starting point. For example, while searching for the armpits as mentioned in the previous sub-section, the position is estimated at about 74.94% and 74.89% of the stature for males and females respectively. Then the mean ± standard deviation would be the reasonable range for the initial search.
Fig. 1. Vitus 3-D 1600 whole body scanning system.
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Table 1 The location estimates used in this study (Wang et al., 2000) Item
Proportion (%) Male
Female
Height of neck to stature Height of shoulder to stature Height of cervicale to stature Height of suprasternale to stature Height of scapular to stature Height of armpit to stature Height of bust point to stature Height of navel to stature Height of hip to stature Height of crotch to stature Height of patella to stature Height of lateral malleolus to stature Length of forearm to the full length of the arm Length of palm to the full length of the arm
84.73 81.90 83.74 80.63 71.39 74.94 71.94 58.81 48.69 42.94 26.43 24.01 58.81 26.18
84.30 81.83 83.46 80.46 71.43 74.89 71.65 58.40 48.79 44.60 26.21 23.86 57.85 26.09
The subjects’ ages range from 18 to 65.
Fig. 2. Silhouette analysis: (a) acromion and intersection of neck and shoulder and (b) chest line.
characteristic line. It can be applied to extract the wrist and waistline. In the range from the middle of the arm to the fingertip, the position with the minimum circumference is identified as wrist. Besides, in the range between the chest line and hip line, the minimum circumference detected is then identified as waistline.
3. Automated landmarking 3.3. Gray-scale detection After initial searches, it is ready for identifying the exact locations of the landmarks. The principle of the proposed method for automated landmarking is to simulate how the landmarks are located in traditional anthropometry. Due to the diversity of body shapes, it is unlikely to use a single method to identify all the landmarks. Thus, in this study, four algorithms are proposed to identify different landmarks. Each algorithm can help to extract several landmarks with similar characteristics. The four algorithms will be described in detail as follows. 3.1. Silhouette analysis Silhouette analysis is enabled by projecting the 3D body scanning data onto a 2D plane. By analyzing the variation in curvature and the depth of the silhouette, landmarks can be located. It can be applied to extract armpits, crotch, intersections of neck and shoulder, acromions, chest line, hip line, and rearmost of hip. For example, when a set of 3D scanning data is projected onto the coronal plane, local maximum and minimum points on the silhouette curves can be used to extract armpits, crotch, intersections of neck and shoulder, and acromions. For another example, when a set of three-dimensional body scanning data is projected onto the sagittal plane, local maximum point on the silhouette can be used to extract the rearmost of hip. In addition, when a set of three-dimensional body scanning data is projected onto the sagittal plane, the characteristic lines including chest line and hip line can be extracted by searching the slice with the greatest depth on the silhouette curve. Fig. 2 illustrates some examples of this algorithm.
This algorithm imitates human visual sense to find the parts with noticeable variation in brightness. It converts the color information from RGB values into gray-scale values to quantify the brightness of skin. It can be applied to extract the bust points for male subjects and the armpits. After detecting the points with smaller gray-scale values, the center points of the two approximate circles near the height of the chest line are extracted as the left and right bust points of a male subject. The armpits are identified by finding the positions with minimum z-value from the detected points near the intersection of torso and arms. The detection results are illustrated in Fig. 3. 3.4. Human-body contour plots This algorithm is to find the convex and concave parts of a human body. After generating the contour plot, it locates the highest and lowest points as the landmarks of human body. This algorithm can be applied to detect the seventh cervical point, bust points for female subjects, inferior angle of scapula, patella, lateral malleolus, elbow, suprasternale, and navel. For female subjects, after rotating the body by 30°, the center positions of the most prominent points near the height of chest line are extracted as left
3.2. Minimum circumference determination This algorithm searches the thinnest part of a body segment to define the location of the landmark and
Fig. 3. Illustration of gray-scale detection: (a) original scan image and (b) points with smaller gray-scale values are detected as the possible landmarks.
J.-M. Lu, M.-J.J. Wang / Expert Systems with Applications 35 (2008) 407–414
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4.1. Linear and curvilinear dimensions
Fig. 4. Human-body contour plots fro locating: (a) bust point and (b) navel.
and right bust points. For another example, the most concave region near the center of the waistline is identified as the location of navel. The detection of the two landmarks is illustrated in Fig. 4. 3.5. Results of landmarking The locations of the extracted 12 landmarks and three characteristic lines are illustrated in Fig. 5. With these key features, the scanning image of human body can be further analyzed for various applications, such as anthropometric data collection. 4. Anthropometric data collection With the 12 landmarks and three characteristic lines extracted, we are able to obtain 104 body dimensions by using the approximation methods. These dimensions can be classified as linear dimensions, curvilinear dimensions, circumferences and cross-sectional areas.
Linear dimensions can be easily obtained by simply calculating the distance between the two landmarks or the vertical distance between one landmark and the floor. Curvilinear dimensions are the arc lengths of the contours of a human body. For example, as shown in Fig. 6, while calculating scye depth, we have to project the three-dimensional whole body scanning data onto the sagittal plane to obtain a two-dimensional contour of the human body. Then, according to the definition of this dimension, the regions between the height of the seventh cervical point and the height of armpits will be considered. Since the contour consists of several connected line segments, the arc length can be obtained by summing up the lengths of these line segments. 4.2. Circumferences and cross-sectional areas Circumferences can be calculated with the convex hull polygonal approximation method. The principle of convex hull polygonal approximation is similar to the procedure we use a tape to measure circumferences. The algorithm of the convex hull polygonal approximation method is illustrated in Fig. 7. The first step is to find out the point with maximum X value from the original data set of a slice, as shown in Fig. 7b. Then we start at this point and trace counterclockwise to find the next edge point on the convex hull polygon. The included angles between the starting point and all of the remaining points are calculated. The point with the minimum included angle is considered as the next edge point, as shown in Fig. 7c. By repeating this procedure, subsequent edge points can be found, and the tracing will be terminated once the new edge
Fig. 5. The extracted 12 landmarks and three characteristic lines.
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point returns to the starting point. After all the edge points are located, as shown in Fig. 7d, the circumference can be approximated by summing up the distances between any two adjacent edge points. 5. System implementation and validation 5.1. System implementation Fig. 6. The calculation of scye depth: original scan (left) and the silhouette projected (right).
The system was implemented using C++ programming language. The total execution time was less than 30 s. Fig. 8 presents the schematic flow of the implemented system, from scanning to anthropometric data collection. To validate the system, we have tested on 189 subjects to evaluate the system’s performance on measuring human-body dimensions. 5.2. System validation
Fig. 7. The process of convex hull polygonal approximation.
For system validation, 189 subjects (120 males and 69 females) aged from 18 to 30 were scanned. Each subject who wore the scanning attire and a cap was measured by both the traditional method and the newly developed method. At first, the locations of the landmarks were premarked with red labels by trained staff. Then the staff took the measurements manually five times for each body dimension. Subsequently, the subject was scanned five times by the 3D whole body scanner. While scanning, the subject was asked to hold his/her breath with the standard standing posture. After scanning, the landmarks were automatically extracted and the anthropometric data were collected by the developed system. Besides, the locations of the pre-marked labels were also recorded for comparison.
Fig. 8. Schematic flow of the implemented system.
J.-M. Lu, M.-J.J. Wang / Expert Systems with Applications 35 (2008) 407–414
5.2.1. Landmarking For the 189 subjects, all the landmarks were successfully located. Namely, the recognition rate was 100%. Then, by calculating the distances between identified landmarks and the pre-marked labels, we were able to test if the identified landmarks had the same positions as the premarked labels taken by the manual method. The mean, maximum, and minimum distance between the detected and pre-specified landmarks was 9.54 mm, 14.55 mm, and 6.75 mm, respectively. Since the label used in the experiment has a circular shape with the diameter of 15 mm, any two points with the difference no greater than 15 mm can be regarded as belonging to the same landmark. In other words, all the landmarks extracted by the proposed method had the same positions as those being used by the manual method. On the other hand, the positions of the extracted landmarks among the five repetitions were compared to evaluate the reliability of the proposed method. The mean, maximum, and minimum value was 1.73 mm, 3.49 mm, and 0.60 mm, respectively. All the distance between any two repetitions was much smaller than the threshold (15 mm), indicating that the proposed method was very consistent for detecting landmarks on human body. 5.2.2. Anthropometric data collection In order to evaluate the accuracy of the measurement results obtained by the proposed method, 12 body dimensions were selected for comparison. The results of the paired T-tests indicated that significant differences were found between the values obtained by the two methods in five dimensions, i.e. shoulder breadth, chest circumference, waist circumference, sleeve length, and anterior cervicale to waist length. But the values of these significant differences did not exceed the tailors’ acceptable criteria (Bradtmiller & Gross, 1999), indicating that the measurements collected by the system are acceptable for garment making in the apparel industry. As for the reliability test, all the 104 dimensions except the cross-sectional areas were selected. Although some of the differences between the repeated scans exceeded the acceptable threshold for scanner-derived measurements (International Organization for Standardization, 2005), they still met the acceptable criteria for manual surveys (Gordon et al., 1989). In other words, the system can generate more reliable measurements than the traditional method. In summary, the newly developed system for automated landmarking and anthropometric data collection was proved to be both effective and robust. 6. Conclusion This study presents the development of a system for automated landmarking and anthropometric data collection from the 3D whole body scanning data. Pre-marking is unnecessary prior to scanning. For automated landmarking, body segmentation and initial searches are first con-
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ducted. Subsequently, four algorithms including silhouette analysis, minimum circumference determination, gray-scale detection, and human-body contour plots are proposed to locate 12 landmarks and three characteristic lines automatically. Further, by using the approximation methods, 104 body dimensions can be obtained. To evaluate the validity and reliability of the newly developed system, 189 human subjects were scanned and tested. And the evaluation results suggest that the system was very effective and robust. Since the newly developed system is fully automated and easy to use, many applications in product design and the medical domain can be extended. References Allen, B., Curless, B., & Popovic´, Z. (2003). The space of human body shapes: reconstruction and parameterization from range scans. ACM Transactions on Graphics, 22(3), 587–594. Bradtmiller, B. & Gross, M. E. (1999). 3D whole body scans: measurement extraction software validation. In Proceedings of international conference on digital human modeling (paper number: 199-01-1892). Hague. Brooke-Wavell, K., Jones, P. R. M., & West, G. M. (1994). Reliability and repeatability of 3-D body scanner (LASS) measurements compared to anthropometry. Annals of Human Biology, 21(6), 571–577. Burnsides, D., Boehmer, M., & Robinette, K. (2001). 3-D landmark detection and identification in the CAESAR project. In Proceedings of the third international conference on 3-D digital imaging and modeling conference (pp. 393–398). Quebec City. Buxton, B., Dekker L., Douros I., & Vassilev T. (2000). reconstruction and interpretation of 3D whole body surface images. In Proceedings of scanning 2000. Paris. Campagne Nationale de Mensuration 2003–2004.
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Research Institute of Human Engineering for Quality Life (HQL).
Wang, M.-J. J., Wang, E. M.-Y., & Lin, Y.-C. (2000). Anthropometric data book of the Chinese people in Taiwan. Hsinchu: The Ergonomics Society of Taiwan. Wang, M.-J. J., Wu, W.-Y., Lin, K.-C., Yang, S.-N., & Lu, J.-M. (2007). Automated anthropometric data collection from three-dimensional digital human models. International Journal of Advanced Manufacturing Technology, 32, 109–115.
Expert Systems with Applications Expert Systems with Applications 35 (2008) 407–414 www.elsevier.com/locate/eswa
Automated anthropometric data collection using 3D whole body scanners Jun-Ming Lu, Mao-Jiun J. Wang
*
Department of Industrial Engineering and Engineering Management, National Tsing Hua University 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan, ROC
Abstract The development of three-dimensional (3D) whole body scanner opens opportunities for measuring human body more efficiently. While taking measurements from 3D scanning data, markers are usually placed on human body to facilitate landmarking and data collection. But the procedure of placing markers is very tedious and may involve human errors. The objective of this study is to develop an automated anthropometric data collection system to eliminate manual intervention. For 3D image analysis, the first step is to segment the body parts by analyzing the 2D silhouette. Then we use the approximate location estimates as the references for performing initial searches of the landmarks. Subsequently, four algorithms including silhouette analysis, minimum circumference determination, grayscale detection, and human-body contour plots are developed to extract 12 landmarks and three characteristic lines on human body. Finally, from the results of automated landmarking, 104 anthropometric data can be obtained. To evaluate the validity and reliability of the automated anthropometric data collection system, 189 human subjects were scanned and the scanning data were processed by the system. The results demonstrated that the system was very effective and robust. Based on the newly developed system, many applications can be extended. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Three-dimensional (3D) whole body scanner; Anthropometry; Landmarking
1. Introduction
1.1. 3D scanning technology for measurements
With the growing trend of globalization, the concept of mass customization in product design is becoming an important issue. For designers, measurements of human body are necessary for the development of well-fitted products, such as clothing and shoes. Traditionally, human-body dimensions are measured manually by using measuring tapes and calipers. However, the procedure is time-consuming and involving direct contact with human subjects. With the advancement of photonics technology, it is now possible to make fast and contact-free measurements by using 3D whole body scanners.
The Loughborough Anthropometric Shadow Scanner (LASS) was one of the first 3D scanners being used for collecting 3D size and shape information of human body (Jones, West, Harris, & Read, 1989). Later on, Research Institute of Human Engineering for Quality of Life (HQL) in Japan conducted a large-scale anthropometric survey with both traditional methods and whole body scanners (Research Institute of Human Engineering for Quality Life (HQL)). In addition, a multinational project, called CAESAR (Civilian American and European Surface Anthropometry Resource), was undertaken from 1998 to 2002 in the US, the Netherlands and Italy (Daanen & van de Water, 1998). They used 3D whole body scanners to gather anthropometric data and construct a database of 3D human models. More recently, a growing number of countries endeavored to use 3D scanners to
*
Corresponding author. Tel.: +886 3 5742655; fax: +886 3 5737107. E-mail address: [email protected] (M.-J.J. Wang).
0957-4174/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2007.07.008
408
J.-M. Lu, M.-J.J. Wang / Expert Systems with Applications 35 (2008) 407–414
conduct national surveys, including China, Korea, Taiwan, UK, US and France (Campagne Nationale de Mensuration, 2003–2004; Fan, Yu, & Hunter, 2004; Treleaven et al., 2004). 1.2. Preparing 3D scanning data In order to make accurate measurements, it is critical to ensure the quality of the scanning images. Since the 3D whole body scanner is an optical measurement equipment, it is very sensitive to the lighting condition and the geometric nature of the scanning object. Thus, strict lighting control and the use of measurement attire for scanning can help to enhance the quality of scanning images (D’Apuzzo, 2005; Min, Nam, & Choi, 2003). Besides, movement artifact is another important factor affecting the quality of scanning images. Asking the subject to adopt standard posture and to hold breath while scanning can help to minimize the effect of body movement (Daanen, Brunsman, & Robinette, 1997). For a commonly used 3D whole body scanner, it can capture more than 300,000 scattered points from the surface of human body. By organizing and analyzing the 3D scanning data, the 3D human model can be constructed, and many applications can be further extended. 1.3. Segmentation of 3D scanning data For the purpose of landmarking and body dimension collection, a 3D human model is usually segmented at armpits and crotch. Five body parts including head and torso, both arms, and both legs can be identified. For body parts segmentation, Nurre, Connor, Lewark, and Collier (2000) proposed a cusp algorithm for segmenting the 3D scanning data of a human body. For each scan, the arms and legs were located at the Scy point and the crotch, respectively. Further, Wang, Chang, and Yuen (2003) applied fuzzy logic concept to locate the armholes and crotch, and then to separate the arms and legs from the trunk. After segmentation, the scanning data will be ready for landmarking.
process and may involve human errors. Therefore, developing marker-free techniques for landmarking becomes an important issue for analyzing the 3D whole body scanning data. For the method of automated landmarking, analyzing the geometry of human body such as the silhouette is a logical approach (Buxton et al., 2000). In addition, researchers also used the method of reconstructing curves and surfaces to locate the landmarks (Douros, Dekker, & Buxton, 1999). But the procedure of curve and surface fitting is complex and time-consuming, and the fitted data may not be able to reserve the geometry of human body exactly. Moreover, a template mapping approach, which makes use of the information from the database of 3D human models, makes it possible to identify landmarks efficiently (Allen, Curless, & Popovic´, 2003). But this technique requires a comprehensive database of 3D human models. 1.5. Measuring the human body Based on the landmarks extracted, measurements can be made by placing a virtual measuring tape or caliper on the surface of human body. Even though the 3D scanner reduces the time and cost for measuring the human body, the accuracy and reliability of the measurements still need to be verified. Brooke-Wavell, Jones, and West (1994) compared the measurements obtained by using 3D whole body scanner and the traditional anthropometry method and found similar results. But traditional anthropometry showed a better repeatability. Moreover, although the repeatability of the scanner-based methods was no better than that of the traditional anthropometry, it could meet the tailors’ acceptance criteria (Bradtmiller & Gross, 1999). Therefore, the use of 3D body scanners for measuring human dimensions can be practical for some applications, such as garment making in the apparel industry. Nevertheless, in order to extend its use as a substitute for traditional anthropometry, improvements are needed. 1.6. Scope of this study
1.4. Landmarking on 3D scanning data Landmarking is to find out the anatomical landmarks that are used to define body size and shape. Since landmarks are mostly bony protrusions, palpation works are needed for identification. In general, anthropologists place markers or stickers on the surface of human body to highlight the positions. Thus the positions of the landmarks can be easily identified on the scanning image with human eyes. Further, by using the color information obtained from the CCD cameras in the scanning heads, the landmarks can also be identified by analyzing the RGB information in the scanning image (Burnsides, Boehmer, & Robinette, 2001; Wang, Wu, Lin, Yang, & Lu, 2007). However, the procedure of placing markers on body surface is a tedious
The objective of this study is to develop a system for automated anthropometric data collection by using the 3D whole body scanner. Prior to data analysis, body segmentation and initial searches are performed with the scanning data. This is given in Section 2. Then, in Section 3, four algorithms are proposed for automated landmarking, including silhouette analysis, minimum circumference determination, gray-scale detection, and human-body contour plots. Further, in Section 4, the detected landmarks enable anthropometric data collection by using the approximation methods. Subsequently, in Section 5, experiments are conducted to validate the system. Finally, the results and important findings are highlighted and concluded in Section 6.
J.-M. Lu, M.-J.J. Wang / Expert Systems with Applications 35 (2008) 407–414
2. Data preparation 2.1. Scanning The 3D scanner used in this study is the Vitus 3-D 1600 whole body scanning system, as shown in Fig. 1. The surface of human body can be captured by four sets of laser beams and CCD cameras in 20 s. In order to obtain optimal scanning image, the subject is asked to adopt a specified posture with standard measurement attire and cap. Arms are kept apart from the torso to facilitate body segmentation. In addition, to minimize the effect of movements, subjects are requested to hold their breaths while scanning. If there is any problem with the posture or if the subject moves, the scanning data will be considered invalid. The number of data points captured varies from 35,000 to 65,000, depending on the volume of the human body. With the CCD cameras, the color information of the human body can be recorded as well. Prior to data analysis, noise has to be removed. The most intuitive method for noise reduction is to compare the distance between any two points with a given threshold. If the distance does not exceed the threshold, they should be considered as neighboring points. Any point without neighboring points is considered as noise and has to be eliminated. 2.2. Segmentation In order to simplify the process of landmarking, the whole body scanning data has to be segmented into five parts, including head and torso, left arm, right arm, left leg, and right leg. Since the head and torso, left arm,
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and right arm are connected at the armpits, it is necessary to locate the armpits before separating these three parts. Accordingly, the crotch, where the torso and two legs meet, is another key location for segmentation. By projecting the 3D scanning image onto the coronal plane, the silhouette of the human body can be obtained. Subsequently, the armpits and crotch can be found at the local maximum points on the silhouette of the scanning image. Then the body parts are segmented along the horizontal planes that pass through the armpits and crotch. 2.3. Initial searches After segmentation, each body part remains to contain more than 10,000 points. Thus, initial searches with approximate location estimates are necessary. The purpose of initial search is to locate the possible positions based on the characteristics of each subject, such as gender and age. The estimates adopted are derived from a comprehensive anthropometric database constructed in 2000 (Wang, Wang, & Lin, 2000). For both males and females, there are 12 vertical location estimates (in proportion to the stature) and two horizontal location estimates (in proportion to the length of a body segment) being utilized, as presented in Table 1. Once the subject’s gender is given, the estimated position of a certain landmark can be applied to locate the starting point. For example, while searching for the armpits as mentioned in the previous sub-section, the position is estimated at about 74.94% and 74.89% of the stature for males and females respectively. Then the mean ± standard deviation would be the reasonable range for the initial search.
Fig. 1. Vitus 3-D 1600 whole body scanning system.
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Table 1 The location estimates used in this study (Wang et al., 2000) Item
Proportion (%) Male
Female
Height of neck to stature Height of shoulder to stature Height of cervicale to stature Height of suprasternale to stature Height of scapular to stature Height of armpit to stature Height of bust point to stature Height of navel to stature Height of hip to stature Height of crotch to stature Height of patella to stature Height of lateral malleolus to stature Length of forearm to the full length of the arm Length of palm to the full length of the arm
84.73 81.90 83.74 80.63 71.39 74.94 71.94 58.81 48.69 42.94 26.43 24.01 58.81 26.18
84.30 81.83 83.46 80.46 71.43 74.89 71.65 58.40 48.79 44.60 26.21 23.86 57.85 26.09
The subjects’ ages range from 18 to 65.
Fig. 2. Silhouette analysis: (a) acromion and intersection of neck and shoulder and (b) chest line.
characteristic line. It can be applied to extract the wrist and waistline. In the range from the middle of the arm to the fingertip, the position with the minimum circumference is identified as wrist. Besides, in the range between the chest line and hip line, the minimum circumference detected is then identified as waistline.
3. Automated landmarking 3.3. Gray-scale detection After initial searches, it is ready for identifying the exact locations of the landmarks. The principle of the proposed method for automated landmarking is to simulate how the landmarks are located in traditional anthropometry. Due to the diversity of body shapes, it is unlikely to use a single method to identify all the landmarks. Thus, in this study, four algorithms are proposed to identify different landmarks. Each algorithm can help to extract several landmarks with similar characteristics. The four algorithms will be described in detail as follows. 3.1. Silhouette analysis Silhouette analysis is enabled by projecting the 3D body scanning data onto a 2D plane. By analyzing the variation in curvature and the depth of the silhouette, landmarks can be located. It can be applied to extract armpits, crotch, intersections of neck and shoulder, acromions, chest line, hip line, and rearmost of hip. For example, when a set of 3D scanning data is projected onto the coronal plane, local maximum and minimum points on the silhouette curves can be used to extract armpits, crotch, intersections of neck and shoulder, and acromions. For another example, when a set of three-dimensional body scanning data is projected onto the sagittal plane, local maximum point on the silhouette can be used to extract the rearmost of hip. In addition, when a set of three-dimensional body scanning data is projected onto the sagittal plane, the characteristic lines including chest line and hip line can be extracted by searching the slice with the greatest depth on the silhouette curve. Fig. 2 illustrates some examples of this algorithm.
This algorithm imitates human visual sense to find the parts with noticeable variation in brightness. It converts the color information from RGB values into gray-scale values to quantify the brightness of skin. It can be applied to extract the bust points for male subjects and the armpits. After detecting the points with smaller gray-scale values, the center points of the two approximate circles near the height of the chest line are extracted as the left and right bust points of a male subject. The armpits are identified by finding the positions with minimum z-value from the detected points near the intersection of torso and arms. The detection results are illustrated in Fig. 3. 3.4. Human-body contour plots This algorithm is to find the convex and concave parts of a human body. After generating the contour plot, it locates the highest and lowest points as the landmarks of human body. This algorithm can be applied to detect the seventh cervical point, bust points for female subjects, inferior angle of scapula, patella, lateral malleolus, elbow, suprasternale, and navel. For female subjects, after rotating the body by 30°, the center positions of the most prominent points near the height of chest line are extracted as left
3.2. Minimum circumference determination This algorithm searches the thinnest part of a body segment to define the location of the landmark and
Fig. 3. Illustration of gray-scale detection: (a) original scan image and (b) points with smaller gray-scale values are detected as the possible landmarks.
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4.1. Linear and curvilinear dimensions
Fig. 4. Human-body contour plots fro locating: (a) bust point and (b) navel.
and right bust points. For another example, the most concave region near the center of the waistline is identified as the location of navel. The detection of the two landmarks is illustrated in Fig. 4. 3.5. Results of landmarking The locations of the extracted 12 landmarks and three characteristic lines are illustrated in Fig. 5. With these key features, the scanning image of human body can be further analyzed for various applications, such as anthropometric data collection. 4. Anthropometric data collection With the 12 landmarks and three characteristic lines extracted, we are able to obtain 104 body dimensions by using the approximation methods. These dimensions can be classified as linear dimensions, curvilinear dimensions, circumferences and cross-sectional areas.
Linear dimensions can be easily obtained by simply calculating the distance between the two landmarks or the vertical distance between one landmark and the floor. Curvilinear dimensions are the arc lengths of the contours of a human body. For example, as shown in Fig. 6, while calculating scye depth, we have to project the three-dimensional whole body scanning data onto the sagittal plane to obtain a two-dimensional contour of the human body. Then, according to the definition of this dimension, the regions between the height of the seventh cervical point and the height of armpits will be considered. Since the contour consists of several connected line segments, the arc length can be obtained by summing up the lengths of these line segments. 4.2. Circumferences and cross-sectional areas Circumferences can be calculated with the convex hull polygonal approximation method. The principle of convex hull polygonal approximation is similar to the procedure we use a tape to measure circumferences. The algorithm of the convex hull polygonal approximation method is illustrated in Fig. 7. The first step is to find out the point with maximum X value from the original data set of a slice, as shown in Fig. 7b. Then we start at this point and trace counterclockwise to find the next edge point on the convex hull polygon. The included angles between the starting point and all of the remaining points are calculated. The point with the minimum included angle is considered as the next edge point, as shown in Fig. 7c. By repeating this procedure, subsequent edge points can be found, and the tracing will be terminated once the new edge
Fig. 5. The extracted 12 landmarks and three characteristic lines.
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point returns to the starting point. After all the edge points are located, as shown in Fig. 7d, the circumference can be approximated by summing up the distances between any two adjacent edge points. 5. System implementation and validation 5.1. System implementation Fig. 6. The calculation of scye depth: original scan (left) and the silhouette projected (right).
The system was implemented using C++ programming language. The total execution time was less than 30 s. Fig. 8 presents the schematic flow of the implemented system, from scanning to anthropometric data collection. To validate the system, we have tested on 189 subjects to evaluate the system’s performance on measuring human-body dimensions. 5.2. System validation
Fig. 7. The process of convex hull polygonal approximation.
For system validation, 189 subjects (120 males and 69 females) aged from 18 to 30 were scanned. Each subject who wore the scanning attire and a cap was measured by both the traditional method and the newly developed method. At first, the locations of the landmarks were premarked with red labels by trained staff. Then the staff took the measurements manually five times for each body dimension. Subsequently, the subject was scanned five times by the 3D whole body scanner. While scanning, the subject was asked to hold his/her breath with the standard standing posture. After scanning, the landmarks were automatically extracted and the anthropometric data were collected by the developed system. Besides, the locations of the pre-marked labels were also recorded for comparison.
Fig. 8. Schematic flow of the implemented system.
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5.2.1. Landmarking For the 189 subjects, all the landmarks were successfully located. Namely, the recognition rate was 100%. Then, by calculating the distances between identified landmarks and the pre-marked labels, we were able to test if the identified landmarks had the same positions as the premarked labels taken by the manual method. The mean, maximum, and minimum distance between the detected and pre-specified landmarks was 9.54 mm, 14.55 mm, and 6.75 mm, respectively. Since the label used in the experiment has a circular shape with the diameter of 15 mm, any two points with the difference no greater than 15 mm can be regarded as belonging to the same landmark. In other words, all the landmarks extracted by the proposed method had the same positions as those being used by the manual method. On the other hand, the positions of the extracted landmarks among the five repetitions were compared to evaluate the reliability of the proposed method. The mean, maximum, and minimum value was 1.73 mm, 3.49 mm, and 0.60 mm, respectively. All the distance between any two repetitions was much smaller than the threshold (15 mm), indicating that the proposed method was very consistent for detecting landmarks on human body. 5.2.2. Anthropometric data collection In order to evaluate the accuracy of the measurement results obtained by the proposed method, 12 body dimensions were selected for comparison. The results of the paired T-tests indicated that significant differences were found between the values obtained by the two methods in five dimensions, i.e. shoulder breadth, chest circumference, waist circumference, sleeve length, and anterior cervicale to waist length. But the values of these significant differences did not exceed the tailors’ acceptable criteria (Bradtmiller & Gross, 1999), indicating that the measurements collected by the system are acceptable for garment making in the apparel industry. As for the reliability test, all the 104 dimensions except the cross-sectional areas were selected. Although some of the differences between the repeated scans exceeded the acceptable threshold for scanner-derived measurements (International Organization for Standardization, 2005), they still met the acceptable criteria for manual surveys (Gordon et al., 1989). In other words, the system can generate more reliable measurements than the traditional method. In summary, the newly developed system for automated landmarking and anthropometric data collection was proved to be both effective and robust. 6. Conclusion This study presents the development of a system for automated landmarking and anthropometric data collection from the 3D whole body scanning data. Pre-marking is unnecessary prior to scanning. For automated landmarking, body segmentation and initial searches are first con-
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ducted. Subsequently, four algorithms including silhouette analysis, minimum circumference determination, gray-scale detection, and human-body contour plots are proposed to locate 12 landmarks and three characteristic lines automatically. Further, by using the approximation methods, 104 body dimensions can be obtained. To evaluate the validity and reliability of the newly developed system, 189 human subjects were scanned and tested. And the evaluation results suggest that the system was very effective and robust. Since the newly developed system is fully automated and easy to use, many applications in product design and the medical domain can be extended. References Allen, B., Curless, B., & Popovic´, Z. (2003). The space of human body shapes: reconstruction and parameterization from range scans. ACM Transactions on Graphics, 22(3), 587–594. Bradtmiller, B. & Gross, M. E. (1999). 3D whole body scans: measurement extraction software validation. In Proceedings of international conference on digital human modeling (paper number: 199-01-1892). Hague. Brooke-Wavell, K., Jones, P. R. M., & West, G. M. (1994). Reliability and repeatability of 3-D body scanner (LASS) measurements compared to anthropometry. Annals of Human Biology, 21(6), 571–577. Burnsides, D., Boehmer, M., & Robinette, K. (2001). 3-D landmark detection and identification in the CAESAR project. In Proceedings of the third international conference on 3-D digital imaging and modeling conference (pp. 393–398). Quebec City. Buxton, B., Dekker L., Douros I., & Vassilev T. (2000). reconstruction and interpretation of 3D whole body surface images. In Proceedings of scanning 2000. Paris. Campagne Nationale de Mensuration 2003–2004.
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Research Institute of Human Engineering for Quality Life (HQL).
Wang, M.-J. J., Wang, E. M.-Y., & Lin, Y.-C. (2000). Anthropometric data book of the Chinese people in Taiwan. Hsinchu: The Ergonomics Society of Taiwan. Wang, M.-J. J., Wu, W.-Y., Lin, K.-C., Yang, S.-N., & Lu, J.-M. (2007). Automated anthropometric data collection from three-dimensional digital human models. International Journal of Advanced Manufacturing Technology, 32, 109–115.