Sage Journals: Discover world-class research

Abstract

This study aims to investigate shape variation at different points of the leg and form a methodology for quantitative characterizing leg shape. 2D leg shapes at 18 points were extracted using the Size Stream Studio software from a body scan. The extracted shapes are the data points of the leg at a height point in space in DXF (Drawing Exchange Format) format. After processing, the shape was centralized with its XY center (0,0) based on its extremes. It was further divided into 36 segments with an increasing angle of 10°, and the circumference of each segment and radius line length were calculated. The variation of the radius length and circumference in each segment was compared between different shapes. The new methodology makes it more accessible as the circular shape leg data was plotted in the linear pattern in the sinusoidal curve, allowing easy identification and comparison of the variation with its location. It will help the patterns to have improved fit and functional garments like compression garment construction with detailed information of shape to accommodate the pressure variation challenges across all points of legs, unlike the radius of curvature to use at only four axis intersecting points in previous studies, it allows making the necessary changes at the knitting stage to have uniform pressure.

Keywords

3D body scanning leg shape compression garment Laplace law Size Stream Studio

Introduction

The application of three-dimensional (3D) body scanning technology has dramatically enhanced the field of anthropometry by enabling the precise capturing of human body contours and measurements^1
–4 which is very popular among researchers. Domingo et al.⁵ proposed a method to build human body prototypes for apparel design based on 3D body scans by emphasizing the significance of cross-sectional analysis at key body points. also developed a 3D model using scan data to construct the pattern for garment construction.⁶ Choi and Ashdown^7,8 further validated the effectiveness of 3D scans in analyzing lower body measurements, particularly for active body positions, highlighting their relevance in both fashion and therapeutic contexts. Scott et al.⁹ suggested improving landmarking using 3D technologies to enhance garment fit. Sun¹⁰ worked on the 3D feature to characterize and classify body shapes corresponding to garment block patterns. Conversely, Hasler et al.¹¹ worked on statistical models of human body shape to provide valuable insights into the challenges of creating well-fitting garments for diverse body types. Domingo et al.⁵ also worked on modeling human body shapes using the random compact mean set concept to define apparel prototypes for apparel design applications. A three-dimensional biomechanical model was developed using Finite Element Analysis (FEA), which uses sleeve deformations, surface pressure magnitude, and distribution of arm and mechanical interactions between the human arm and sleeve during wear.¹² Estimating the top parts of socks pressure is also done using FEA by curve fitting method of human body deformation and corresponding displacement on the lower leg under pressure using a 2D simulation FEM model of the human body consisting of skin, soft tissue, and bones.¹³ The lower limb body shape using digital anthropometry was simulated in 2D image simulation to calculate the curvature and radius of curvature.¹⁴ The finite element (FE) model is used to predict the compression effects of sportswear, providing insights into contact pressure distribution and garment deformation.⁷ FEA-based biomechanical model to predict garment pressure in compression therapy, highlighting the importance of accurate geometric representations of body parts obtained through 3D scanning and imaging technologies.^12,15
–17 Dan et al. support using 3D models to simulate and analyze garment pressure on different body parts.¹⁸ Garrigues-Ramón et al. investigated Laplace’s law and found it provides high-pressure results as compared with those obtained through pneumatic sensors.¹⁹ Chrimes et al. found that the same body shape experiences similar challenges regarding fit.²⁰

The human leg cross-section at a given point is not a circular shape. It presents more complex geometry that varies with the leg’s measurement point—dividing the leg’s cross-section into four equal quadrants ranging from 0° to 90°, 90° to 180°, 180° to 270°, and 270° to 360°. ²¹ Each quadrant displays distinct attributes. The anterior section may protrude more due to the quadriceps muscles, while the posterior section may appear flatter due to the hamstring muscles. The asymmetry of the leg shape results in an uneven distribution of its circumference around the central point. It leads to an elliptical or irregular shape determined by the underlying muscle and bone structures.^22,23

When making textile clothing for the leg, especially tight-fitting items like tights or compression garments/stockings, the leg’s non-circular/irregular cross-section needs to be considered. The leg’s shapes and dimensions must be adapted to ensure functionality, fit, and comfort^{7,13,24
–27} Tight body garments may apply varying degrees of compression depending on the wearer’s circumference; hence, accurate measurements are crucial when selecting a compression garment. Along with the circumference measurements, the body’s shape is also essential when discussing pressure applied by compression garments.²⁸ Identically sized garments could fit differently on various body shapes.^29,30 There is a need for standardized methods to classify and analyze body shapes across different populations and contexts .⁶ The classification and analysis of body shapes play a pivotal role in garment design, especially in creating patterns that fit various body types.^8,31 Currently, only circumference measurements are considered important for products like compression garments, and no methodology exists to explain the variation of leg shape at different points. Assessing the complexity of the human leg’s shape in a two-dimensional cross-section by determining the radius length and the length of the curved line in each quadrant can yield the necessary information for sports, medical, and textile design applications in garments. Accommodating the variation in leg shape patterns is important to improve the fit. For functional garments like compression garments, the pressure applied by the garment also depends on the shape. This study develops a systematic approach to the methodology/process for analyzing leg shapes, their variation, and similarity at each point along the leg to achieve the required pressure for compression garments, while accounting for variation in leg curvature and angles across leg slices. Circular leg shape variation was quantified linearly across 360° of the leg to make the necessary adjustment in pattern or at the knitting stage for compression garments.

Methodology

Size Stream Studio 5.2.9. Software was used to analyze the shapes of lower-body scans from CESAR data. Eighteen shapes from different body measurement points, from the ankle to the crotch point, were analyzed. The predefined shapes in the Size Stream Studio^1,20 software includes the minimum leg circumference, ankle, calf, knee, mid-thigh, and thigh. These measurement points were identified based on the definitions provided within the Size Stream software, as outlined in Table 1. Additionally, new measurement points were established by using the height of these predefined points and defining new measurements at specific heights. Three additional measurements at equal heights from ankle to calf, Calf to Knee, Knee to mid-thigh, and mid-thigh to crotch point (C01) were also studied to understand the variation of shapes between these measurement points. In addition, the shape of the minimum circumference of the leg at the ankle point was also studied. The list of these measurement points is given in Table 1 below. Each measurement is assigned a code consisting of the first letter of the measurement name and its family serial number for the Size Stream Studio built-in measurement. Numbers 1, 2, and 3 are appended to the lower measurement names between the two measurements for newly created measurements. The scan displays all measurements in Size Stream Studio in Figure 1.

Table 1.

Measurement names and code.

Sr. no.	Measurement code	Measurement name
1	A20	Minimum leg girth above the ankle and below the knee
2	A10	Circumferences at ankle bones
3	A10.1	First measurement between Ankle and Calf
4	A10.2	Second measurement between Ankle and Calf
5	A10.3	Third measurement between Ankle and Calf
6	C20	Maximum leg girth above the ankle and below the knee
7	C20.1	First measurement between Calf and Knee
8	C20.2	Second measurement between Calf and Knee
9	C20.3	Third measurement between Calf and Knee
10	K01	Circumference at the mean height of both kneecaps
11	K01.1	First measurement between Knee and Mid-thigh
12	K01.2	Second measurement between Knee and Mid-thigh
13	K01.3	Third measurement between Knee and Mid-thigh
14	M01	Taken at the mid-point between the Knee and Thigh
15	M01.1	First measurement between Mid-Thigh and Thigh
16	M01.2	Second measurement between the Mid-Thigh and Thigh
17	M01.3	Third measurement between the Mid-Thigh and Thigh
18	T01	Leg girth measured 2 inches below the Crotch

Figure 1.

Front, back, and side view of measurements extracted for analysis.

The Size Stream Studio feature enables users to visualize a scan in 3D and provides tools for manually measuring dimensions, such as circumference, height, and shape. It uses the scripted code to evaluate its built-in measurements, which are already part of its library. Additionally, it enables the creation of a customized script that enhances the accuracy of measurements, whether taken in the same or different scans, by applying scientific anthropometric principles. The Size Stream Studio was customized with a script that could recognize the measurement, and each shape was then retrieved manually at specific measurement points. The actual data point of the 3D shape in 3D space was extracted using a customized Python script. MATLAB was used to centralize the 2D shapes such that the center on the x- and y-axes was zero, with the four sides lateral, posterior, medial, and anterior. The data points were then plotted to recreate the leg shape.

The newly plotted shapes were divided into 36 segments at an equal angle of 10° in an anticlockwise direction, as shown in Figure 2 for a circle. While any number of segments could be selected, 36 segments were chosen for this study because they allow easy division of the circle and keep the calculations manageable. The length from the center (0,0) to 36 points on the circumference of the leg shape, that is, the radius from the center to the outer shape, was measured along with the length of the curve, that is, the circumference length of 36 segments. The mean of the 36 points was determined for the circumference and the length from the center to the circumference, along with the mean of each of the 4 quadrants, that is, 0°–90°,90°–180°,180°–270°,270°–360°. The actual and mean data differences were subsequently computed and displayed in MATLAB. The minimum, maximum, and mean values were calculated to determine whether there is high variation in the radius length and curve across a shape, or whether they are uniform, as reflected in the differences among these three values.

Figure 2.

Thirty-six length radius and circumference segments.

Results and discussion

The results are presented in four tables. The first three tables provide data on the length of radius, and the outer boundary circumference divided into six segments, and the fourth table contains all the plots of leg shape and the differences from the mean value. This study analyzes detailed measurements of the lower leg shape at 18 measurement points from the ankle to the thigh, focusing on the radius and the circumference across 36 segments. Table 2 shows mean, minimum, and maximum values, that is, radius length and circumference at 18 measurement points. Consistent increase in radius and curve length as it moves from the Ankle (A10) toward the thigh (T01) indicates growth in leg circumference. It also provides the comprehensive distribution and variation of measurement across 360° at an interval of 10°; these measurements can be used to interpret leg shape characteristics and variation. The results show that the radius length increased consistently from Ankle (A10) to the calf, decreased from C20.3, and increased again from K01 to the thigh point. Interestingly, a similar trend for the curve length measurement is attributed to the direct relation of radius length to curve length. As the radius length increases, the curve length for measuring the human lower body legs also increases, as observed in circular objects. Uniformity can be referred to as the circularity of shape. Lowering the variation in these values will reflect a high circularity of the shape and vice versa. Overall, the results of Table 2 show that measurements A20, A10, A10.3, K01.1, C20, and K01 have a high variation percentage, with variations of 43% and 71%, 54% and 25%, 23% and 52%, 31% and 29%, 27% and 26%, 27% and 26% respectively for both radius and curve length. It shows that a measurement with a high variation % has non-circularity, likely coinciding with higher or lower pressures.

Table 2.

Mean, minimum, and maximum results of the radius and curve length.

Code	Radius length (cm)					Curve segment length (cm)
Code	Min	Mean	Max	Var (%)	CV (%)	Min	Mean	Max	Var (%)	CV (%)
A20	2.73	3.34	4.16	42.81	13.94	0.49	0.65	0.95	70.77	17.68
A10	2.78	3.51	4.66	53.56	13.61	0.57	0.67	0.74	25.37	8.57
A10.1	3.24	3.78	4.25	26.72	8.89	0.73	0.82	0.87	17.07	4.52
A10.2	4.19	4.65	5.01	17.63	4.70	0.81	0.94	1.02	22.34	5.61
A10.3	4.63	5.37	5.87	23.09	5.96	0.48	0.61	0.80	52.46	15.06
C20	4.88	5.89	6.49	27.33	7.21	0.86	1.04	1.13	25.96	6.61
C20.1	5.09	5.84	6.24	19.69	5.13	0.89	1.02	1.09	19.61	4.71
C20.2	5.18	5.66	6.09	16.08	4.71	0.91	0.99	1.07	16.16	4.68
C20.3	4.97	5.51	6.22	22.69	6.75	0.87	0.97	1.08	21.65	6.95
K01	4.89	5.70	6.41	26.67	7.84	0.86	1.01	1.12	25.74	7.55
K01.1	5.02	6.06	6.88	30.69	8.43	0.89	1.08	1.20	28.70	7.79
K01.2	5.58	6.44	7.01	22.20	6.10	1.01	1.14	1.22	18.42	5.68
K01.3	6.29	6.87	7.30	14.70	5.14	1.11	1.21	1.28	14.05	4.89
M01	6.73	7.38	7.97	16.80	5.52	1.17	1.30	1.38	16.15	5.29
M01.1	7.33	8.11	8.82	18.37	6.04	1.28	1.43	1.54	18.18	5.81
M01.2	8.09	8.82	9.43	15.19	4.93	1.42	1.55	1.64	14.19	4.70
M01.3	8.40	9.24	9.74	14.50	4.13	1.47	1.62	1.69	13.58	3.87
T01	8.36	9.20	9.70	14.57	4.26	1.46	1.61	1.69	14.29	4.01

Interestingly, the variation in radius line length and curve length increases/decreases at measurements in similar trends. In contrast, for some other measurements, it is different, which will be explained later in the next section. But it can be observed that both of these variations become closer to each other when the variation % is small, less than 20%, that is, C20.1, C20.2, K01.3, M01, M01.1, M01.2, M01.3 and T01 which also shows that the shape of this measurement point is more circular having less sharp variation point in that measurement. On the other hand, there is no significant trend of variation percentage if the center of the curve length is higher.

The Coefficient of Variation (CV%) is a standardized measurement of the dispersion of the dataset. Like the Var %, it is also high for measurements like A20, A10, A10.3, etc., for both measurements. On the other hand, overall variation is low for measurement from Calf C20 toward the thigh, ignoring the Knee measurement, that is, K01 and K01.1. The lowest values were observed for T01.3 and T01, both of which correspond to thigh measurements. This indicates lower surface deviation from an ideal circular profile, reflecting a more uniform radial distribution at these anatomical locations. From the CV% results, it was also difficult to conclude that, if the radius length% increases, the curve length% will increase.

Quadrants

The distance from the center of the cross-section to the outside boundary at nine equally spaced places in each quadrant as shown in Figure 3, and calculating the mean of these distances can lead to the information of shape asymmetry and irregularity. These measurements can provide essential information and a comprehensive profile of the leg’s shape, which is vital for designing functional garments.

Figure 3.

Division of a circular shape into four quadrants of each 90° and further breaking into different part with 10°.

The four legs sides are named posterior, medial, anterior, and lateral. The anterior side is the front of the leg at 90° of the plotted shape, whereas the Posterior is the back side at 270°; the medial side would be the inner side of the body at 0°, whereas the lateral is the outer side of the leg shape in 180°. Leg distribution about these four sides is unequal, and having a different radius length measured and mean at nine different locations shows that from A20 toward T01 at all measurement points, the mean length between anterior and lateral was highest, quadrant two (Q2). The overall minimum and maximum distance from the center length to the outer boundary can be compared to identify the shape symmetry and irregularities. The higher the distance of minimum and maximum from the mean, the higher the irregularity and vice versa. The highest and lowest mean values also indicate the overall convex or concaveness of the leg shape. The concaveness and convexness effect will be higher when there are the highest differences in the mean value in quadrants, and the minimum when there are less differences in each quadrant’s mean value, with more minor differences in the minimum and maximum value from the mean value (Table 3).

Table 3.

Mean, minimum, and maximum results of the radius length of each segment.

Code	Radius length (cm)
	Q1			Q2			Q3			Q4
	Min	Avg	Max	Min	Avg	Max	Min	Avg	Max	Min	Avg	Max
A20	2.80	3.34	3.99	2.75	3.76	3.91	2.73	3.14	3.86	2.81	3.46	4.16
A10	3.18	3.74	4.19	3.06	3.81	3.99	2.96	3.21	3.83	2.78	3.51	4.66
A10.1	3.36	3.61	4.08	3.34	4.17	4.25	3.24	3.59	4.08	3.51	3.97	4.24
A10.2	4.19	4.47	4.88	4.58	4.77	5.01	4.46	4.51	4.56	4.55	4.74	4.88
A10.3	4.63	4.98	5.39	5.38	5.69	5.77	5.20	5.24	5.30	5.34	5.63	5.87
C20	4.88	5.33	5.92	5.98	6.32	6.46	5.74	5.85	5.93	5.76	6.21	6.49
C20.1	5.09	5.44	5.89	5.78	6.16	6.24	5.73	5.83	5.90	5.78	6.05	6.18
C20.2	5.18	5.37	5.86	5.36	5.99	6.09	5.41	5.73	5.91	5.48	5.81	5.99
C20.3	4.97	5.26	5.97	4.99	6.03	6.22	5.11	5.71	5.98	5.23	5.51	5.88
K01	4.89	5.52	6.41	5.14	6.16	6.38	5.33	5.95	6.32	5.47	5.64	6.12
K01.1	5.02	5.71	6.81	5.37	6.67	6.88	5.89	6.28	6.62	5.77	6.20	6.33
K01.2	5.58	6.21	7.01	6.06	6.80	6.97	6.12	6.49	6.94	6.07	6.58	6.78
K01.3	6.29	6.69	7.23	6.51	7.24	7.27	6.34	6.67	7.27	6.62	7.04	7.30
M01	6.79	7.22	7.84	6.91	7.81	7.87	6.73	7.18	7.86	7.00	7.59	7.97
M01.1	7.40	7.88	8.67	7.58	8.74	8.82	7.33	7.91	8.69	7.67	8.30	8.72
M01.2	8.09	8.48	9.11	8.49	9.37	9.43	8.13	8.55	9.10	8.60	9.11	9.35
M01.3	8.40	8.86	9.52	9.01	9.67	9.71	8.83	9.10	9.41	9.03	9.52	9.74
T01	8.36	8.83	9.50	8.94	9.65	9.68	8.76	9.04	9.40	8.97	9.48	9.70

The measurements of the curve lengths for each segment are provided in Table 4. The curve length does not exhibit the same patterns as the radius and mean curve length. Specifically, in Q4, the curve length for A20 is higher. The mean length of the curves for A10, K01.3, and M01.2 was larger in quarters Q2 and Q4. However, for A10.1, the mean curve length is higher in quarters Q1 and Q4. Higher values were seen in Q2 for A10.2 and M01.1. Q4 had higher values for A10.3, C20, C20.1, M01, M01.3, and T01. C20.2 showed higher values in Q3 and Q4. Q3 had higher values for C20.3, K01, and K01.1. K01.2 had higher values in Q1, Q3, and Q4. Lower variability in curve lengths between quartiles, as indicated by smaller differences between the minimum and maximum values from the mean value, suggests that the shape is more uniformly distributed. Conversely, greater differences suggest less uniformity. It can also be observed that the difference between the minimum maximum and mean values kept decreasing when we moved from the ankle toward the thigh, but it was high around the knee region; lower this difference indicates the increase in circularity of the shape.

Table 4.

Mean, minimum, and maximum results of the curve length of each segment.

Code	Curve segment length (cm)
	Q1			Q2			Q3			Q4
	Min	Avg	Max	Min	Avg	Max	Min	Avg	Max	Min	Avg	Max
A20	0.48	0.61	0.80	0.48	0.60	0.68	0.49	0.59	0.74	0.49	0.63	0.80
A10	0.57	0.68	0.74	0.58	0.70	0.74	0.57	0.64	0.72	0.61	0.70	0.74
A10.1	0.49	0.67	0.95	0.53	0.62	0.70	0.52	0.62	0.95	0.49	0.67	0.91
A10.2	0.73	0.81	0.87	0.82	0.86	0.87	0.78	0.79	0.79	0.80	0.83	0.85
A10.3	0.81	0.95	1.02	0.94	0.98	1.00	0.91	0.91	0.93	0.93	0.99	1.02
C20	0.86	1.04	1.13	1.04	1.08	1.12	1.00	1.02	1.03	1.03	1.09	1.13
C20.1	0.89	1.03	1.09	1.00	1.05	1.09	1.01	1.02	1.03	1.03	1.06	1.08
C20.2	0.91	1.00	1.07	0.94	1.00	1.06	0.96	1.01	1.03	0.95	1.01	1.04
C20.3	0.87	0.98	1.08	0.87	0.97	1.08	0.93	1.01	1.04	0.91	0.96	1.02
K01	0.86	1.02	1.12	0.90	1.00	1.11	0.96	1.06	1.10	0.96	0.99	1.08
K01.1	0.89	1.09	1.20	0.94	1.08	1.20	1.05	1.11	1.15	1.05	1.09	1.10
K01.2	1.01	1.15	1.22	1.06	1.13	1.21	1.07	1.15	1.22	1.08	1.15	1.18
K01.3	1.11	1.21	1.28	1.13	1.23	1.26	1.11	1.18	1.28	1.15	1.23	1.27
M01	1.17	1.30	1.38	1.20	1.31	1.37	1.17	1.27	1.37	1.22	1.32	1.38
M01.1	1.28	1.43	1.54	1.32	1.46	1.54	1.28	1.40	1.52	1.33	1.44	1.52
M01.2	1.42	1.55	1.64	1.48	1.59	1.64	1.42	1.51	1.60	1.50	1.59	1.63
M01.3	1.47	1.62	1.69	1.57	1.65	1.69	1.54	1.59	1.65	1.57	1.66	1.69
T01	1.46	1.62	1.69	1.55	1.64	1.69	1.53	1.58	1.65	1.56	1.65	1.69

Table 4 shows all the 18-leg shape plots, segments’ circumference, and radius length. Parallel to this, two plots show the difference between the radius and curve length from the mean. If a shape is circular, the line length difference will be horizontal at y equals 0. The difference plot makes it easier to visually see the difference between the actual quantification of the shape variation and the exact location. The difference in radius length will also highlight the concaveness and convexness of shape at a region with its precise value. It helps quantitatively to identify the concaveness and convexness of the shape. Below the line from the mean line, that is, “0” will indicate the concaveness of shape, and higher it is from the mean line will indicate the convexity of shape. Observing the radius length difference shows a shift in the concavity and convexity of the leg shape when moving from one measurement point to another across its circumference. The highest convexness was observed for the ankle measurement A10 with a difference of radius length of 1 cm, whereas the concaveness was observed for shapes A10, A10.3, C20, K01.1, and K01.2 with the convexness value near −1. However, considering the actual shape measurement, the percentage of concaveness will be higher for shape A10, but it was higher for C20 and K01.1 with the value of −1.

It isn’t easy to compare the whole shape to its original closed form. The other important thing to observe is the sharpness of the concaveness or convexness. The plots identify the sharpness, concaveness, and convexness, as well as their location and quantification, compared to the rest of the shape. After the knee points K01.1, the circularity of the shapes increases, and there is less variation from the mean radius length. Two significant peaks in lower leg points are observed when expressing the leg shape’s convexity. For body points A20, A10, and A10.1, the two sharp peaks were observed around the same region, in which the first peak was around 90° and the second peak around 300°. These two peaks were also observed around 180° and 0°, reflecting the shape’s concavity. For points A10.2, A10.3, C20, and C20.1, there were no significant convex peaks, and the shape is much more circular as compared with the lower points, but a short peak expressing the concavity of shape around 50°–60° was observed at all four points. For points C20.2, C20.3, K01, and K01.1, the shape changes with one significant peak expressing the convexity of shape again around 90°, and for all of the rest of the points, the shape is more circular, and there is no significant shape peak of both concavity or convexity of the shape (Table 5).

Table 5.

Leg shape and radius length and curve length difference from the mean value.

Code	Leg shape segmentation	Radius and curve segment length differences:	Radius and curve segment length differences (%)
A20
A10
A10.1
A10.2
A10.3
C20
C20.1
C20.2
C20.3
K01
K01.1
K01.2
K01.3
M01
M01.1
M01.2
M01.3
T01

Discussion

In previous studies, pressure at different measuring points was measured by other authors considering the effect on shape using different forms derived from Laplace law.^32
–34 Majorly it was measured at four different locations around a shape in opposite directions. However, the challenge is that the shape variation is not only at four locations but is continuous, and measuring at four locations might not be a true representation. On the other hand, the extreme pressure profiles are often not along the axis intersection point, which will also not be a true representation of the extreme pressure as found in previous literature.^23,35
–37 The variation in shape and angles across a leg is continuous and changes at all points along its circumference. The methodology described in this study provides a solution to the constant variation in leg shape across its circumference and addresses the limitations of Laplace-based theories. Significant differences in leg shape from ankle to thigh affect clothing design and functionality. The results reveal variation in measures of curve length and radius length between and within segments, indicating non-uniformity in leg shape that may impact garment fit and function. Modifying the garment pattern while considering the variance in these segments is necessary to achieve an appropriate fit. Failing to include this variation may result in the garment twisting, or in the seams looking twisted and not fitting correctly on the leg for cut-and-sewn garments. The findings indicate that the design and efficacy of compression garments may be substantially influenced by the intricate variation between various leg segments at multiple points. Variations within segments highlight the difficulty in producing garments that can consistently conform and exert compression for pressure garments, which is essential to ensure both comfort and effectiveness in therapy. It is important to consider these variances when designing functional clothing, such as compression garments, to maintain a constant pressure distribution. For instance, areas with greater girth may require garments with higher elasticity or custom areas with larger loops, different knitting parameters, or different fabric compositions to maintain the desired compression levels. The detailed breakdown by dividing the leg into 4 parts and further into 36 segments allows for a more precise understanding of these variations. Graphical views, which feature a more granular division of 36 segments, offer insights into the fine-scale variability of leg shapes. This detailed segmentation highlights areas where the leg’s shape varies and could exert differential pressures when a compression garment is worn, which is critical for garment design. It is particularly relevant for custom-fitted compression garments where precision in fit and compression profile can significantly influence effectiveness. The extraction and analysis of 2D shapes from 3D body scans offer a robust framework for understanding human body morphology. It has significant implications across various fields, particularly in garment design, where accurate body shape representation is essential for creating well-fitting, comfortable clothing. Continued advancements will undoubtedly lead to more personalized and practical apparel and ergonomic design solutions.

Conclusion

In this study, a new methodology was designed to explain the variation of circular shapes like legs at different points. The new approach has allowed the quantification of leg shape variation from close shape to linear form, significantly improving its understanding. Fundamental geometric concepts can be effectively used to improve the knowledge of human body shapes, particularly when characterizing or comparing two or more individuals using sinusoidal wave representations. Leg shape was divided into four quadrants, and it was found that the shape distribution is not uniform; the conventional garment patterning concept using circumference measurements and splitting into two patterns limits the garment fit by the possibility of seams shifting as leg shape distribution changes which was found highest around the ankle, calf knee and mid-thigh points whereas at C20.2 the mean curve length was found to be highly uniform. For functional garments like compression garments, the pressure applied by the garment is highly dependent on the garment’s shape. This study helps characterize the shape in a very definite way, which can be further related in the future to compression variation across the whole leg circumference. The previous studies used the Laplace function, in which the radius of curvature was used at four-axis intersection points, but it does change at every point, which shows the limitation to explain actual results. Another problem is that there is no standard agreement on how to use data points around a point to calculate the radius of curvature, since different data points yield different radii of curvature at a given point. The curves on both sides of the legs differ, which makes it challenging to represent the actual shape using a radius of curvature. Findings from this research shed light on the shape variation; using them will help describe the pressure profiles of compression garments. This approach can support product development and improve existing products by refining their pattern construction and modifications for different body shapes.

Future research

Future research should focus on correlating these physical measurements and shape variation to further refine compression garment specifications. The shape variation across individuals of the same and different sizes also needs to be explored. The significant differences in radius and curve segment lengths from the ankle to the thigh can help quantify the pressure variation across the leg which needs to be explored. Customized garments that account for these variations can provide better fit and more effective compression, enhancing both comfort and therapeutic outcomes. Pressure distribution verification across the different shapes of the human body using customized knit garments accommodating the current suggestion needs to be studied in the future.

Footnotes

ORCID iD

Zeeshan Azam

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

Gill

A review of research and innovation in garment sizing, prototyping and fitting. Text Prog 2015; 47(1): 1–85.

Gill

Hayes

Lower body functional ease requirements in the garment pattern. Int J Fashion Des Technol Educ 2012; 5: 13–23.

Suh

Mannequin fabrication methodology using 3D-scanning, modeling and printing. Int J Clothing Sci Technol 2021; 33: 683–695.

Guarnieri Lopez

Matthes

Sob

, et al. Associations between 3D surface scanner derived anthropometric measurements and body composition in a cross-sectional study. Eur J Clin Nutr 2023; 77: 972–981.

Domingo

Ibáñez

Simó

, et al. Modeling of female human body shapes for apparel design based on cross mean sets. Expert Syst Appl 2014; 41: 6224–6234.

Hlaing

Krzywinski

Roedel

Development of 3D virtual models and 3D construction methods for garments. In: 2nd International conference on 3D body scanning technologies, Lugano, Switzerland, 25–26 October 2011, pp. 25–26.

Lin

Choi

Luximon

, et al. Finite element modeling of male leg and sportswear: contact pressure and clothing deformation. Text Res J 2011; 81: 1470–1476.

Kim

Choi

Park

, et al. Standardization of the size and shape of virtual human body for apparel products. Fashion Text 2019; 6(1): 20.

Scott

Schildmeyer

Ruderman

, et al. Proposed landmarking for improved digital product creation. Commun Dev Assem Text Prod 2023; 70–87.

10.

Sun

Cai

, et al. Body shape classification and block optimization based on space vector length. Int J Cloth Sci Technol 2019; 31(1): 115–129.

11.

Hasler

Stoll

Rosenhahn

, et al. Estimating body shape of dressed humans. Comput Graph 2009; 33: 211–216.

12.

Zhao

Zhang

, et al. A three-dimensional biomechanical model for prediction of garment pressure in pressure therapy for burn patients. Therm Sci 2020; 24: 2357–2365.

13.

Zhang

Dong

Fan

, et al. Finite element simulation on clothing pressure and body deformation of the top part of men’s socks using curve fitting equations. Int J Clothing Sci Technol 2015; 27: 207–220.

14.

Liu

Lao

, et al. Determination of leg cross-sectional curvatures and application in pressure prediction for lower body compression garments. Text Res J 2019; 89: 1835–1852.

15.

Kennedy

Hwaung

Kelly

, et al. Optical imaging technology for body size and shape analysis: evaluation of a system designed for personal use. Eur J Clin Nutr 2020; 74(6): 920–929.

16.

Lee

Song

Kim

Categorization of lower body shapes of abdominal obese men using a script-based 3D body measurement software. Fashion Text 2020; 7: 3.

17.

Sobhiyeh

Kennedy

Dunkel

, et al. Digital anthropometry for body circumference measurements: toward the development of universal three-dimensional optical system analysis software. Obes Sci Pract 2021; 7: 35–44.

18.

Dan

Fan

Shi

, et al. Study on the relationship between pressure and stiffness coefficient, and elastic elongation of the top part of men’s socks using finite element method. Int J Cloth Sci Technol 2015; 27(5): 751–766.

19.

Garrigues-Ramón

Julián

Zaragoza

, et al. Inability of Laplace’s law to estimate sub-bandage pressures after applying a compressive bandage: a clinical study. J Wound Care 2021; 30: 276–282.

20.

Chrimes

Boardman

McCormick

, et al. Investigating the impact of body shape on garment fit. JFMM 2023; 27: 741–759.

21.

Kimura

Cross-section of human lower leg bones viewed from strength of materials. J Anthropol 1971; 79: 323–336.

22.

Mersmann

Bohm

Schroll

, et al. Muscle shape consistency and muscle volume prediction of thigh muscles. Wiley Online Libr 2010; 25: e208–e213.

23.

Dubuis

Avril

Debayle

, et al. Identification of the material parameters of soft tissues in the compressed leg. Taylor Fr 2012; 15: 1–15.

24.

Hidayati

Anistyasari

Body shape calculator: understanding the type of body shapes from anthropometric measurements. In: Proceedings of 2021 International conference on multimedia retrieval, 2021, pp.461–465: ICMR 2021.

25.

Kang

Kim

Analyzing the changes in anthropometric measurements with various postures using three-dimensional scanning technology. J Eng Fiber Fabr 2023; 18: 1–15. DOI: 10.1177/15589250231165990

26.

Sommer

Wong

, et al. Detailed 3-dimensional body shape features predict body composition, blood metabolites, and functional strength: the shape up! studies. Am J Clin Nutr 2019; 110: 1316–1326.

27.

Ashby

Jake LaPorte

Richardson

, et al. Translating digital anthropometry measurements obtained from different 3D body image scanners. Eur J Clin Nutr 2023; 77(9): 872–880.

28.

Brubacher

Tyler

Apeagyei

, et al. Evaluation of the accuracy and practicability of predicting compression garment pressure using virtual fit technology. Cloth Text Res J 2023; 41: 107–124.

29.

Brubacher

Apeagyei

Venkatraman

, et al. Design of sports compression garments: exploring the relationship between pressure distribution and body dimensions. In: 8th Asia-Pacific congress sport technology, Tel Aviv, Israel, 2017.

30.

MacRae

Laing

Partsch

General considerations for compression garments in sports: applied pressures and body coverage. In: Engel

Sperlich

(eds) Compression garments in sports: athletic performance and recovery. Cham: Springer International Publishing, 2016, pp.1–32.

31.

Liu

Guo

Peng

, et al. Stratified body shape-driven sizing system via three-dimensional digital anthropometry for compression textiles of lower extremities. Text Res J 2018; 88: 2055–2075.

32.

Bera

Chattopadhay

Gupta

Effect of linear density of inlay yarns on the structural characteristics of knitted fabric tube and pressure generation on cylinder. J Text Inst 2015; 106: 39–46.

33.

Richards

Steele

Spinks

GM.

Experimental evaluation and analytical model of the pressure generated by elastic compression garments on a deformable human limb analogue. Med Eng Phys 2020; 83: 93–99.

34.

Rajendran

Anand

SC.

Developments in medical textiles. Text Prog 2002; 32: 1–42.

35.

Ghorbani

Hasani

Jafari Nedoushan

Finite element modelling the mechanical performance of pressure garments produced from elastic weft knitted fabrics. J Text Inst 2019; 110: 724–731.

36.

Tamoue

Ehrmann

Blachowicz

Predictability of sub-bandage pressure in compression therapy based on material properties. Text Res J 2019; 89: 4410–4424.

37.

Brubacher

Towards the design of sports compression garments with controlled pressure. Doctoral dissertation. Manchester Metropolitan University, 2018, pp.1–408.

Shape investigation of the human lower body using segmentation principles

Abstract

Keywords

Introduction

Methodology

Results and discussion

Quadrants

Discussion

Conclusion

Future research

Footnotes

ORCID iD

Funding

Declaration of conflicting interests

References