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Finite element modelling of Chinese male office workers necks using 3D body measurements

Finite element modelling of Chinese male office workers necks using 3D body measurements
Finite element modelling of Chinese male office workers necks using 3D body measurements

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The Journal of The Textile Institute

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Finite element modelling of Chinese male office workers’ necks using 3D body measurements

Shanshan Wang, Yingjiao Xu & Hongbo Wang

To cite this article: Shanshan Wang, Yingjiao Xu & Hongbo Wang (2017) Finite element modelling of Chinese male office workers’ necks using 3D body measurements, The Journal of The Textile Institute, 108:5, 766-775, DOI: 10.1080/00405000.2016.1186911

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The Journal of The TexTile insTiTuTe, 2017Vol. 108, no. 5, 766–775

https://www.wendangku.net/doc/875930467.html,/10.1080/00405000.2016.1186911

Finite element modelling of Chinese male office workers’ necks using 3D body measurements

Shanshan Wang a , Yingjiao Xu b and Hongbo Wang a

a

Key laboratory of science & Technology of eco-textiles Ministry of education, Jiangnan university, Wuxi, China; b Department of Textile and apparel, Technology and Management, College of Textiles, north Carolina state university, raleigh, nC, usa

ABSTRACT

Recognizing the influence of occupational habits on human morphology, there has been a discernible increase in research taking anthropometric body measurements of a target population for the purpose of customized product development and production to meet different customer needs. This study aims to develop a 3D neck model for the Chinese young male office workers with a goal to provide a tool to maximize the ergonomic fit and comfort of the collar part of apparel products. A total of 200 male Chinese office workers meeting the sampling criteria were recruited for this study. Using factor analysis, the raw 3D measurements were reduced to a six-factor seven-measure model, capturing majority of the neck structure information. Based on these 7 neck measurements, the 200 subjects were classified through K means cluster analysis into 4 clusters. The cluster with largest number of subjects was chosen for the 3D neck model development. This 3D model includes three layers: the skin layer, the soft tissue layer and the skeleton layer. Comparing to 2D neck models, this three-layer 3D neck model provides a better and closer imitation of real human necks, permitting simulation and investigation of the pressure-deformation process that a neck experiences during wearing.

Introduction

In recent years, more and more people are using computers with poor head posture or looking at their mobile phones with their heads tilted downwards (Hhansraj, 2014). This type of posture goes against the normal cervical shape, thus causing neck mor-phological changes (Rothenberg, 2014; Walter, 2013). Research showed that about half of the Chinese in-patients with cervical spondylosis were young people under the age of 40 (Ma, 2005) with occupations associated with long sitting or long period of computer usage. Recognizing the influence of occupational hab-its on human morphology (Blair et al., 2015), there has been a discernible increase in research taking anthropometric body measurements of a target population and its division into homo-geneous groups for the purpose of customized brand development and apparel production to meet different customers’ expectations.Sizing system development aims to establish sizing systems of whole body or body segments from the anthropometric data of a population using various approaches (Salehi & Maryam, 2012).Three-dimensional anthropometry is the science of measuring and quantifying the human body or body segments in a three-di-mensional space (Landes, Trolle, & Sader, 2012). In contrast with traditional linear anthropometry, 3D anthropometry brings tremendous benefits to accommodate diverse groups within a population, and leads to improvement in the product ergo-nomic design. With the development of 3D scanning technology,

it is now possible to generate a large database of 3D models of humans with different demographic backgrounds (Niu, Zhang, & Wu, 2009).

Research on ergonomic design is not new. However, research on neck modelling is relatively limited. Previous studies usually focused on 2D models around the girth section of the neck (Wu, Zhao, & Chen, 2011). But, the analysis of a single section can’t provide comprehensive information on the state of the neck. Three-dimensional models were mostly used in studies involving the torso, limbs and other body parts (Lin, Choi, et al., 2011). The development of 3D models of the neck has not been established systematically. Additionally, previous research on neck modelling was based on general population (Wu et al., 2011). Neck studies for certain target population are inadequate. This study, using 3D measurements, aims to develop a neck model for a particular market segment in China – male office workers age 25–30, whose work and lifestyles make them vulnerable to neck morphological tensions and changes (Fox News, 2014).

Research methods

The objective of this study was to develop a three-layer 3D neck model for Chinese young male office workers. Considering the variation in the structure of necks among Chinese young males, neck classification was first conducted before the 3D model

? 2016 The Textile institute

KEYWORDS

Chinese young male office workers; 3D body measurements; finite element; 3D neck model development

ARTICLE HISTORY

received 16 november 2015 accepted 2 May 2016

CONTACT Yingjiao xu

yxu11@https://www.wendangku.net/doc/875930467.html,

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(Alexander, 1994; Brown, Cash, & Mikulka, 1990; Ferguson et al., 2012 and Jiang, Zhang, Xia, & Hou, 2008). In this study, a factor analysis with varimax rotation was conducted on the 11 measurement parameters. The analysis suggested a 5-factor model with 57.5% of the variance explained (Table 3).

The factor loading matrix is presented in Table 4. Items for each factor were determined using 0.7 as the cutoff for factor loading coefficient (Fornell & Larcker,1981; H arman, 1976; Manly, 2005 and Rencher, 2012).

A review of the selected items indicated the five factors responsible for the neck shape from the following aspects: neck girth, neck depth, neck breadth, front neck length and back neck length. Recognizing the forward tilting tendency of male office workers’ necks, the researchers added another aspect to reflect the neck shape: neck inclination, measured by back neck angle (BNA). Therefore, this study used six factors together to reflect the neck shape. Table 5 presents the six factors and items loaded on each factor.Cluster analysis

Based on the six factors identified in the factor analysis, a k-means cluster analysis was conducted to classify the type of the 3D shape of necks. To increase the accuracy of the results for a k-means cluster analysis, a Q-type cluster analysis was conducted to deter-mine the number of classifications from the clustering (Clary & Wandersee, 2013).A comparison of classification trees gener-ated for two-group, three-group and four-group classifications suggested a four-group classification for this study. Therefore, a four-group k means cluster analysis was conducted (Tuxhorn, 2008). Table 6 summarizes the results of the cluster analysis.As shown in Table 6, there were59, 51, 53 and 37subjects clas-sified into the first, second, third and fourth clusters, respectively. It seems that the neck girth and neck inclination played a very important role in distinguishing individuals with different neck

development. Therefore, there are two parts in this study: (1) neck classification; and (2) neck modelling.

To classify the neck types, 3D neck measurements were first obtained from a sample and then a cluster analysis was conducted on the reduced 3D neck measurements obtained through factor analysis. The modelling process was based on one of the neck types identified in the neck classification process and involves the following three steps: (1) image acquisition, (2) transformation of image to data and (3) modelling. The following sections provide more details about neck classification and neck modelling.

Neck classification 3D neck measurements

A convenience sample of 200 Chinese male office workers aged between 25 and 30 with body index of 175/92A was recruited for this study. Subjects were recruited through e-mails from an e-mail list which was obtained from the researchers’ institution. The par-ticipants were offered a financial incentive of 50 yuan RM

B for their time and effort in participating in the study. Following this, participants were scanned using the three-dimensional (3D) body scanner housed in the college. Data were collected over a period of 4 weeks. Table 1 presents the characteristics of the sample in terms of occupation, age, height, weight and BMI.

Neck information from each subject was collected using the Vitus Smart non-contact laser 3D body scanner made by Human Solution, Germany. Each measurement was conditioned at the ambient temperature of (20 ± 2) °C and a relative humidity of (60 ± 10)% for more than 24 h to meet the environmental stand-ards for nude measurements (Wang & Wang, 2013; Zouhour, Marc, Chang, & Richard, 2006).

According to the anthropometric base parameters for basic human body measurement for technological design (GB/T 5703-2010), 11 direct measurements were obtained from a 3D scan to characterize a neck. Table 2 lists the full names and the abbrevi-ated names for the 11 measurements.

Recognizing the variation in the structural shape of necks, instead of developing a 3D model for all necks, this study first classified the necks into different groups and developed a 3D model for the largest group. To ease the process of classifica-tion through cluster analysis, a factor analysis was conducted to reduce the dimension of the 11 neck measurement parameters.

Factor analysis (FA)Factor analysis was frequently used in anthropometry s tudies to reduce the dimension of original measurements Table 1. Demographic and occupation information of sample members.Subjects Age (years)Height (cm)Weight (kg)BMi occupations

Percentage (%)

200

27.23 ± 3.23

177.1 ± 1.29

67.05 ± 1.39

21.21 ± 1.45

Teachers 22.50Text workers

17.50Graphic designers

16information technology professionals 14assembly-line workers 13finance professionals 10.50Drivers 5others

1.50

Table 2. Measurement parameters.nos.full name

Abbreviation 1front neck length fnl 2Back neck length Bnl 3front neck angle fna 4Back neck angle Bna 5Middle neck girth MnG 6root neck girth rnG 7Middle neck depth MnD 8Middle neck breadth MnB 9root neck depth rnD 10root neck breadth rnB 11

side neck length

snl

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conditions. Cluster 1 contained necks with shortest girth, while Cluster 4 contained the longest girth. Cluster 2 contained the necks with largest back neck angle and Cluster 3 contained the smallest back neck angle. Figure 1 displays the profile plots for the four clusters.

Among these four distinctive groups, Cluster 1 was selected as the base to develop the 3D model, as it represents the larg-est group and having least number of outliers. The basic body data and standard deviation for Cluster 1 are listed inTable 7. It is worth mentioning that, while this study only developed a 3D model for Cluster 1, the same modelling procedure can be applied to develop models for other clusters.

Neck modelling

To establish the 3D neck model for the largest cluster (Cluster 1), 20 subjects from that cluster were invited back to do a sec-ond scanning. The first scanning was only to get measurements for the 11 parameters. This second scanning was to provide a more detailed capture of the 3D neck shapes. The modelling process involves the following three steps: (1) image acquisi-tion, (2) transformation of image to data and (3) modelling. The following softwares were employed in this modelling process: Scanworx by H uman Solution Germany, MATLAB 2012 by MathWorks, Corel DRAW 12 by Corel Canada and SolidWorks

Table 4. rotating factor loading matrix.

note: The items loaded in each factor are marked to bold values by using 0.7 as the cut off for factor loading coefficients.

Component

12345Bnl ?0.092?0.107?0.7340.1040.045fnl 0.043?0.064?0.0920.798?0.020fna 0.0510.6100.073?0.359?0.117Bna ?0.0470.4410.1800.5200.000MnG 0.799?0.034?0.0320.0180.098rnG 0.741?0.0620.1090.153?0.310MnB 0.014?0.030?0.1520.0130.805MnD ?0.026?0.0320.559?0.0380.586rnB 0.5830.152?0.056?0.1160.082rnD 0.0460.737?0.0780.1590.040snl

?0.239?0.2020.4920.266?0.214

Table 5. six factors and seven items on each factor.factors Back length factor

front length factor

girth factor Breadth factor

Depth factor

Angle factor

items

Bnl

fnl

MnG and rnG

MnB

rnD

Bna

Table 6. final cluster centres.

note: numbers in the parenthesis after each cluster represent the size of the cluster in terms of the number of subjects and the percentage of the sample population.

Cluster

1 (59, 29.5%)

2 (51, 25.5%)

3 (53, 26.5%)

4 (37, 18.5%)

Bnl 6.12 6.21 6.23 6.25fnl 8.378.518.128.49Bna 22.3132.199.2517.90MnG 35.8936.5936.5037.65rnG 42.7944.3043.0748.40MnB 11.7411.5411.6811.54rnD

16.38

16.04

15.70

16.39

Table 3. eigenvalue variance decomposition.

Component

1 1.64914.99114.991 1.60214.56614.566

2 1.26411.48726.479 1.19710.88525.452

3 1.21211.01537.493 1.18610.77836.230

4 1.14010.36647.859 1.18110.73346.963

5 1.0699.71557.574 1.167

10.611

57.574

60.9638.75066.32470.8948.12674.45180.8177.42881.87990.7867.14389.022100.694 6.31395.33511

0.513

4.665

100.000

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neck curvature based on this range is considered reflective of the real condition when clothing item is worn (Werneburg et al., 2014). The 3D neck model established in this study covers the neck portion between the bottom edge of the occipital bone and the NRG section (Figure 2). Therefore, the neck portion covered in this model is inclusive and reflective of all possible

collar heights. To maximize the similarity between the model and

the real neck, the 3D model established in this study includes three layers: a skin layer, a soft tissue layer and a skeletal layer.

14.0 by SolidWorks U.S.A. A high-performance graphics work-station(AW-670), which has the following configurations: Intel processor 3.2 GHz, 2 GB memory, 300 GB double disc, 256 M FX3450 graphics card and Dell Tm 24-inch monitor.

Image acquisition – cross-sectional point cloud figures

Collars of clothing items are usually located between the NMG and NRG sections of the neck (Zhong, 2000

). Modelling of the Figure 1. Profile plots of clusters.

Table 7. Basic body data and standard deviation of Cluster 1.Test project

Min Max Mean Standard deviation

Bna/cm 16.427.522.3 2.9MnG/cm 32.439.936 1.8rnG/cm 38.44741.7 2.4height/cm 175180177.1 1.29Body weight/kg

627467.05 1.39head circumference/cm 59.662.160.850.73BMi/(kg m ?2)

19.04

24.24

21.21

1.45

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Chen, 2012), the reading points on the cross section point

cloud figure were identified every 5° by MATLAB as shown in Figure 3(c). So, for each cross section, there were 72 reading points. For each reading point, the number of white pixels between the centre black pixel and the edge black pixel was obtained to represent the distance from the reading point to the centre of the point cloud circle. The distance from the cen-tre of the circle (point cloud figure) to the edge of the circle was calculated by applying the ratio of pixels to a centimetre (1 cm = 51.26 pixels).Modelling

Skin layer modelling

The skin layer model of the neck was established based on data from the 40 cross-sectional point cloud figures. The 3D coordi-nates X , Y , Z for each measuring point Q on each cross section of the skin layer were obtained as below: where, D q : the distance from the edge point q to the centre point of the skin layer point-cloud figure; θq : the angle corresponding to each edge point q ; H i : the sagittal axis value of cross section i (the vertical distance of each section from the bottom edge of the occipital bone)

Using the above formula, the 3D coordinates for all the 40 cross sections were obtained for the skin layer, and were then entered into SolidWorks 14.0 to output the neck skin layer 3D model. Figure 4 depicts the skin layer sectional split diagram of a male neck. A 3D skin layer model is depicted later when the three-layer model is presented (Figure 6).

(1){X =D q ?cos q ,Y =D q ?Sin q ,Z =H i },The skin layer was developed based on the information col-lected on the neck skip. Specifically, in this study, point cloud figures obtained from the neck skin were used to represent the closed curve of the neck cross section. To maximize the capture of the structure and shape of the whole neck, point cloud figures were obtained every 0.3 cm along the neck from the lower edge of the occipital to the end of the root neck girth section. The point cloud figures were obtained using the Scan Worx software com-ing with the 3D body scanner. As the subjects were from the same cluster, they had similar neck length. The 0.3-cm segmentation resulted 40 cross sections for all the 20 subjects.Transformation of image to data

The point cloud figure of each section took a rough shape of a circle reflecting the shape of the neck section. Although the neck shapes of the subjects are similar, a complete match among all the subjects cannot be achieved. Therefore, the graphics acquired from the Scanworx were processed (aver-aged) to obtain a standard cross-sectional shape of the neck skin layer to represent the cluster. The processing of image average is shown in Figure 3.

Figure 3(a) represents a cross-sectional point cloud figure (extracted using Corel DRAW12) from the Scanworx output. Colouring treatment was applied to obtain the grayscale images as shown in Figure 3(b).The system defined each section centre of the circle (marked by a black pixel) and provided the rectangular coordinate axes of the neck cross section (Fang, Zhao, Ocegueda, Shah, & Kakadiaris, 2012).

After several preliminary experiments and reference to the relevant literature (Lin et al., 2011 and Dan, Fan, &

Figure 2.

range of the 3D neck model.

Figure 3. The processing of image average. (a) The cross-sectional point cloud figure of neck (b) The cross-sectional grayscale image of neck (c) The reading points processing.

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tissue layer of the male neck were calculated using the following formula: where, D q : the distance from the edge point q to the centre point of the skin layer point-cloud figure; θq : the angle corresponding to each edge point q ; H i : the sagittal axis value of cross section i (the vertical distance of each section from the bottom edge of the occipital bone)

Skeletal layer modelling

Subsequently, to establish the skeletal layer model, a standard anatomical male neck skeleton (Figure 5.) was used to obtain the point cloud image. The outer section of the cervical spine was intercepted to obtain the 3D model by superposition. The position of the cervical spine on the cross section of the neck’s skin layer was determined based on anatomical literature (Faiz & Simon, 2011). The 3D models that contained the layer of the epidermis and bone were then created.

Following a similar approach used in other studies in developing limb models(Lee, Zhang, Jia, & Cheung, 2004 and Zhang, Liu, & Tan, 2003), a final 3D model was established using SolidWorks 14.0 to include three different layers: a skin layer, a soft tissue layer and a skeletal layer. This combined model (Figure 6) provided a comprehensive representation of the neck.

Model application

This three-layer 3D Finite Element model can provide great guidance and assistance in ergonomic design of clothing collars, such as predicting neck deformation cuased by clothing(collar) pressure, developing body mapping apparel (collar), etc. In this paper, an application of the model in predicting neck deforma-tion was performed.This neck deformation prediction process included the fol-lowing steps: (1) development of test clothing, (2) measurement of wearing pressure, (3) simulating the wearing by applying the measured pressure on the 3D neck model and (4) prediction of neck deformation. CMS530 HP made by Stoll was used to make the test clothing for Step 1. In step 2, AMI3037 S-5 sup-plied by AMI Limited Company in Japan was used to measure

(2){X o = D q ?0.18 ?Cos q q ,Y o

= D q ?0.18 ?Sin q a ,Z o =H i }.Soft tissue layer modelling

Adopting the average skin thickness of 1.82 ± 0.29 mm (Li, Li, & Lu, 2008), a soft tissue layer 3D model was established from the skin layer model.The3D coordinates (X o , Y o , Z o ) of the soft

Figure 4.

skin layer sectional split diagram of a male neck.

Figure 5.

standard anatomical male neck skeleton.

Figure 6. final model created in solidWorks 14.0.

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in Table 9. CMS 530 HP made by Stoll, Germany was used to make the knit turtleneck sweaters.Measurement of wearing pressure

A pressure measuring device AMI3037 S-5 supplied by AMI Limited Company in Japan was used to measure the objective clothing pressure in this study. The air sac sensors with 2-mm thickness were placed between the skin surface of the subject’s neck and the inner layer of the turtleneck sweater for measuring the clothing pressure exerted on the neck. The output voltage signal data were recorded through the AMI system and the sig-nal were processed by a special voltage amplifier to indicate the pressure the neck received.

Given that the neck is symmetrical, only the clothing pressure on the left side of the neck was measured in this study, assum-ing the pressure on the other side to be the same. As shown in Figure 8, nine important muscle points (labelled from b 1 to b 9) on the left side of the neck were selected to measure the pres-sure received. These muscle points were selected due to their important roles played in the neck when the neck moves (Fung, 1993). The experiments were conducted in a quiet room, with the measurement ambient temperature of (27 ± 3) °C and the relative humidity of (60 ± 10)% with no wind(Landes et al., 2012).

Five subjects were conditioned in the testing environment for 10 min before the wearing. The test point positions were calibrated with tapes (Liu, Chen, Wei, & Pan, 2011). When the subjects wore sweaters for testing, the wearing sequence and test sequence were randomized to avoid any untoward influences on the results. Thereafter, the data were recorded three times at each point when the pressure value was stable. The average data of the clothing pressure exerted on all subjects’ necks by each knit turtleneck sweater are shown in Table 10. It is noted that the closer to the b 9 point, the greater the wearing pressure.Wearing simulation on the 3D neck model

ANSYS was used to conduct the wearing simulation on the 3D model by applying the pressures obtained from the wear trial as depicted in Table 10. All materials in the three layers of the model are considered as homogeneous, isotropic and linearly elastic in this study (Liu, Chen, Wei, & Pan, 2013). Table 11 details the complete material properties and elemental types of the com-ponents of the model. The Y oung’s modulus, Poisson’s ratio and

pressures. Finally, the ANSYS software was used in the last two steps to apply the pressure on the neck model and obtain the neck deformation.

Development of test clothing

The test clothes take the form of tight knit turtleneck sweaters as shown in Figure 7. As this research focused only on the neck section, the test clothes differ only in the length of the collar to represent different pressures applied to the neck. The collar height and the remaining part of the clothes were kept the same across all the test clothes.

The collar length was determined using the following formula

where, L : the collar length; l H : the head circumference; E : the

fabric elongation

The head circumference was taken as the mean of the 20 sub-jects in Cluster 1(head circumference = 60 cm) (refer to Figure 1 and Table 7). Seven different collar lengths were designed to reflect different levels of fabric elongation for the collar, as dis-played in Table 8.

The yarns used in making the sweater have 95% cotton/5% spandex makeup, reflecting the common years used for sweaters in the market. Other specifications of the test clothes are included

(3)L =l H ∕E

,Figure 7. Chart pattern of knit turtleneck sweater.

Table 8. Collar specifications for test clothing.note: The above calculation is based on the head circumference of 60 cm.

Test clothes number S 1S 2S 3S 4S 5S 6S 7elongation 5%10%15%20%25%30%35%Collar length

57 cm

54 m

51 m

48 m

45 m

42 m

39 cm

Table 9. Product specification for the knit turtleneck sweater test clothes.Yarns

Knit structure Yarn count

Yarn type needle fabric thickness (mm)

Cotton (95%)1+1rib

32s

single yarn

12 gg

3.4

spandex (5%)

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loading in the ANSYS impulation process. The nine pressure values from each testing cloth were applied to the left side of the 3D model and duplicated on the right side of the model to simulate the pressure received by the whole neck model. As a result, the system generated the displacement values to represent the neck deformation resulted from the applied loading. Figure 10 depicts the deformation values corresponding to the seven different pressures for each of the nine measuring point on one side of the neck.

A noticeable pattern was recognized in Figure 10. Consistent across the seven pressure loadings, the biggest

deformation occurred in the adjacent area of b 9. The areas

adjacent to b 3 and b 5 displayed the smallest deformation. The area covering b 1, b 2, b 7, b 8 and b 9 constituted the largest dis-placement of the neck.Not only outputting displacement values for each of the

nine measuring points, more importantly the ANSYS system

density for skin, soft tissue and bone are set in reference to the existing literature (Pan, Zan, & Foster, 1998).

Also, consistent with the literature (Fung, 1993 and Ishimaru, Isogai, & Matsui,2009) on mesh structure for elastic materials, triangle elements were used to mesh the skin, soft tissue and bones of the neck, as depicted in Figure 9. To facilitate the imple-mentation, the extremely complex curved bones were simplified so that the number of elements and nodes were not listed on the Table 11.

Prediction of neck deformation

As the purpose was to investigate the deformation of the neck resulting from clothing pressure exerted by the sweater, the pres-sure due to supporting body weight should not be simulated (Finnie, 2000 and Lin et al., 2011). The pressure experimen-tal datum obtained from the wear trial (Table 10

) was used as Figure 8. neck pressure measurement position.

Table 10. Contact pressures exerted on neck by each knit turtleneck sweater (kPa).Test site S 1S 2S 3S 4S 5S 6S 7b 10.1830.2250.1900.2410.3130.3270.387b 20.1830.2480.2560.2720.3340.3500.368b 30.1510.1660.2010.2190.2930.2950.307b 40.1750.2340.2230.2350.2890.3500.374b 50.1600.2270.2190.2550.3490.3400.420b 60.1800.2540.2230.2550.3110.3610.439b 70.2050.2820.2500.2870.3050.3730.396b 80.2000.2750.2680.2840.3500.3570.376b 9

0.269

0.297

0.341

0.339

0.374

0.386

0.459

Table 11. element information and material characteristics of Chinese male professionals’ neck.Model layers number of nodes

number of elements

element type Material Young’s modulus (Kpa)Poisson’s ratio Density (mg/

mm 3)skin

5120825272shell elastic 1500.46 1.03soft tissue 114,50378863solid elastic 24.50.451Bone

solid

elastic

4.79

0.3

1.2

Figure 9. a finite element model of the Chinese male professionals’neck.

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This 3D model includes three layers: the skin layer, the soft tissue layer and the skeleton layer. The skin layer model was developed based on the point cloud figures obtained from the skin of the necks of the subjects from the chosen cluster. The tissue layer was further developed from the skin layer by consid-ering the skin thickness. A standard anatomical male neck skel-eton was used to establish the skeleton layer model. Comparing to 2D neck models, this three-layer 3D neck model provides a better and closer imitation of real human necks, permitting simulation and investigation of the pressure-deformation pro-cess that a neck experiences during wearing. For example, by applying wearing pressure to the 3D model, this study simulated and obtained the deformation occurred on the neck. This simu-lation can provide great inputs for ergonomic design for collars,

ties and scarves. Given the availability of 3D design technology,

the deformation map generated from the simulation on the 3D model provides great potential for smooth integration of the information obtained from the model to the knitting process.Limitation and future work

This study fills a gap in the literature on investigating the pressure and deformation the neck receives during the wearing process by establishing a three-layer 3D neck model. However, there are few limitations that future studies can help to overcome. First, due to time and budget restraints, the 4 neck types were identified based on only 200 eligible subjects. A large sample can help to assure a better reflection of the neck types among Chinese young male

can also generate a deformation map reflecting the different rates of deformation occurred on different parts of the neck. As an example, Figure 11 depicts the neck deformation when loaded with pressure corresponding to test cloth S 6. The defor-mation was colour coded, with red representing the biggest rate of deformation. This 3D colour coded deformation map reflected the findings indicated in Figure 10, but with more information and better visual representation. With the avail-ability of 3D knitting design technology, the vast information contained in this 3D deformation figure can provide great inputs to the collar design with a goal to maximize ergonomic comfort as well as fit.

Conclusion

In summary, this study developed a three-layer 3D neck model for Chinese young male office workers. Considering the variation among the male necks, this model was developed for the most common type of neck structure. To identify the most common neck type for Chinese young male office workers, 200 qualified

subjects were recruited to get their 3D neck measurements. Using

factor analysis, the raw 3D measurements were reduced to a sev-en-measurement model (MNG, RNG, RND, BNL, FNL, MNB and BNA), capturing majority of the neck structure information. Based on these seven neck measurements, the 200 subjects were classified into four clusters in terms of their neck structure. The cluster with largest number of subjects was chosen for the 3D

neck model development.Figure 10.

Vertical displacement values of nine test points.

Figure 11. Vertical displacementof the Chinese male professionals’ neck (S 6).

D o w n l o a d e d b y [U n i v e r s i t y o f J i a n g n a n ] a t 21:21 30 O c t o b e r 2017

THe JoURnAl of THe TeXTile inSTiTUTe 775

Jiang, L., Zhang, W ., Xia, Ming, & H ou, X. (2008). Neck characteristic

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China.

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Wang, S. S., & Wang, H. (2013). Neck morphology research of on-the-job young men in Jiangsu and Zhejiang areas by means of three-dimensional anthropometry. Journal of Textile Engineering, 34, 131–136.

Werneburg, I., Hinz, J. K., Gumpenberger, M., Volpato, V ., Natchev, N., & Joyce, W . G. (2014). Modeling neck mobility in fossil turtles. Journal of Experimental Zoology, 324B , 230–243.

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office workers. Second, this study only developed a model for the most common neck type. Future studies definitely can develop

a model for each neck type identified in this study. Third, future studies could use wear trial to provide further validation of this three-layer 3D neck model. Finally, future studies could focus on integrating the 3D information generated from the model into

the 3D knitting design.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was supported by the Jiangsu Provincial Graduate Student

Research Innovation Projects of China [CXZZ13_0750].

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