Design and Development of a CAD/CAM System for Foot Orthotics

Paul Omari Mogeni, Vickneswari A/P Durairajah, Suresh Gobee

Asia Pacific University

Technology Park Malaysia

Bukit Jalil, Kuala Lumpur 57000

Malaysia

glpaul701@gmail.com, vicky_nesa@apu.edu.my, suresh.gobee@apu.edu.my

ABSTRACT

The aim of the project is to design, develop and

test scanning equipment to acquire three

dimensional data of a foot sole surface, and to

use CAD and CAM tools to design a digital

orthoses shell with its geometry adapted from

digital 3D scan data. Experimental tests are also

done to test the reliability and repeatability of

foot surface where data generated from 30 sets

of measurements is analyzed using various

statistical tools such as standard deviation,

correlation and regression analyses. Results

show that a strong positive and linear correlation

exist between computer generated measurements

and the mean of manual measurement.

KEYWORDS:

Orthoses, Point cloud, Mesh, Cad-Cam

1. INTRODUCTION

Foot orthotics is a branch of the medical

field that is concerned with the analysis,

design and manufacture of devices used to

guide and to reduce the weight bearing

forces as experienced when conduction daily

activities . Generally speaking, orthoses are

used to control aid or assist in functional or

structural movements of the skeletal system

of concern. Orthoses can either be pre-

fabricated where they are mass produced

and available off the shelf for as general

purpose, or they can be customized to meet

individual cases. In both cases, a detailed

analysis of the patient’s foot must be taken

in order to detail the correct correctional

specification. This project proposes an

effective approach that aims to address the

efficiency of the design phase of customized

correctional shoe inserts or lasts for infant

foot orthotics in terms of development time,

cost and degree of simplicity. The aim of the

project is to design, develop and test a

CAD/CAM system for to acquire three

dimensional data of a foot sole surface and

to use this data for foot orthotic design and

morphology purposes. To achieve this, the

following objectives are to be met: To scan

the foot sole surface and produce its digital

image. This involves capturing the digital

three dimensional data of the foot sole

surface using scanning equipment using the

principle of laser triangulation with low cost

and off the shelf equipment. To process raw

scan data and visualize it in 3D space. The

data processing involves integrating

mathematical models and computational

algorithms to the analysis and 3D solid

modeling stages. To test the reliability of

computer generated measurements against

manual measurements. To generate machine

tool instructions based on the 3D digital

model of the orthoses.

2. PREVIOUS WORK

A fundamental significance of applying

principles of CAD/CAM in the design and

development of foot orthoses equipment is

to optimize on time and cut down on design

and development costs. In article [1] a

podiatrist who previously relied on

traditional method of design and fabrication

of sport related orthoses implemented a

CAD/CAM system that comprised of a 3D

contact digitizer, image processing software

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and a host computer in the design of

orthoses. Subsequently an efficacy of over

97% was reached as a result of a more

efficient design and modeling process.

Orthoses design encompasses foot

measurement or classification as a primary

objective for instance, [2] applied CAD by

using a 3D scanner to develop a new sizing

system for Taiwanese males. Anatomical

landmarks physically placed on the foot

assisted in acquiring foot measurements

after which K – Means cluster and principle

component analysis techniques were applied

to classify feet of male. Overall, an

agreement of 72.96% was reached. [3] also

categorized foot scans according to their

characteristics and highlighted the

advantages (improved comfort) of this

approach in the design of customized foot

orthotics. In contrast to [2], [3] implemented

posture and geometry analysis using CAD

software and used this to evaluate similarity

of 3D shoe lasts. In both studies, the positive

outcomes reflect by having reduced

development times and light computational

processes as measurements can be readily

extracted from similar and existing designs

stored in a database. Other primary uses of a

scanning system other that capturing 3D

data include classifying feet [3] according to

geometry deriving measurements. A viable

technique is to use physical anatomical

markers to assist the data acquisition system

in locating and collecting foot

anthropometric data as described by [4].

With the markers fixed at known positions

on the foot, the system developed by this

author was able to compute the required

dimensions and classify foot shapes

according to [5] the principle component

analysis algorithm. According to the study,

use of markers increases the accuracy of

results but with a tradeoff between the

degrees of consistency and increased lead

times. Foot shape, form and size can be

determined using image processing

techniques such as surface or edge detection.

Such an approach has been experimented by

[6] where the authors combined boundary

mesh detection and the region growing

method of the edge and face based

techniques respectively, a hybrid approach

used to detect both sharp and smooth edges.

The boundary mesh is detected using the

cost criterion algorithm. Also placing

anatomical points on the foot before

scanning may be necessary for data

acquisition and also to check for

repeatability or consistency of results.

During implementation phase, acquired

measurements will also be compared to

manual measurement in order to test for

accuracy reliability of the results.

3. METHODOLOGY

3.1 Point Registration - Concept

To define spatial vector coordinates of a

point on the scan surface, phase angle

theorem is used with triangulation equations.

Consider the diagram shown in figure 3.1

which shows the spatial positions of the

optical sensor, laser emitter and foot surface

relative to each other.

Figure 3.1: Visual representation of the laser light

triangulation theorem

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To determine the vector notation of a point

A on the surface, the shortest distance l

between the baseline and the point is first

determined according to equation 3-1:

(3-1)

3.2 System Design

Figure 3.2 illustrates the 3D CAD design

and the positioning of the man system

components.

Figure 3.2: CAD design representing form and shape

of the 3D foot scanner

The design and fabrication of the scanner’s

electronic and hardware components is done

by applying the total design approach. This

is to ensure that it meets its primary purpose

with minimal systematic errors. The

software component is built and tested using

C-language scripts using the PIC16F777A

microcontroller.

3.3 Calibration Tests and Procedures

Prior to scanning, calibration procedures are

executed in order to determine the optimum

relative position of the camera with

reference to the laser centre and the angle

between each. The camera is first calibrated

to ensure reliability and consistency of

scanning results and also to set datum points

within the computational software. This

enables the software to compute the distance

between the camera centre and the location

of the scan object. The first test is done to

determine the most effective position to fix

the camera, the incident angle of the laser

line to the foot surface and the perpendicular

distance from the camera centre to the foot

surface. A scanning object is positioned at a

distance of 150mm (l on figure 3.3) from the

camera’s optical centre. The lateral distance

between the camera and laser centres is first

fixed at 200mm (vector a).

Figure 3.3: Triangle of vectors representing positions

of the camera, laser and object.

The angle the laser projector makes with the

horizontal axis is varied accordingly and

outputs is noted and recorded at a point

where the laser line remains strongly

detected as can be seen on feedback given

by the registration software.

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4. GEOMETRY CAPTURE AND

ORTHOSES DESIGN

Foot sole scanning tests are conducted using

the experimental set up shown in figures

4.1a and 4.1b. Experimental tests are done to

analyze the reliability and repeatability of

using the laser triangulation theorem to

capture 3D surface data of the foot sole

surface. A repeatability test is conducted to

in order to gauge whether the system’s

instruments are operating as expected. To

conduct such a test, successive scans are

taken while maintaining the alignment of the

foot specimen and the scanning equipment.

Figure 4.1a: Foot sole sample used for testing

purposes

Figure 4.1b: Experimental set up used to perform foot

surface scans

Once a scanning cycle is complete, cloud

point data is imported into the post-

processing software where mesh and surface

profiles are generated to create a 3D surface

model.

4.1 CAD/CAM APPLICATION

CAD/CAM tools are used to process raw

scan data to a useful form. As illustrated in

figure 4.2. The first step of this process

involves eliminating background noise and

unwanted detail from the 3D cloud data,

next is to filter the point cloud data if

necessary after which a polygon mesh is

generated. Edge detection and NURB (Non

Uniform Rational B-Splines) constructing

tools are then be used to highlight edges,

boundary extraction and model any curved

surfaces respectively. Once the 3D surface

model is defined, it is used to design foot

orthoses that can be used as a correctional

insole when manufactured. To facilitate the

production of such a product, machine tool

programming may be required. Generation

of machining instructions is also done and is

based on the scanned foot profile

Figure 4.2: Image processing procedure

The collection of surface vector points in 3D

space is shown in figure 4.2.

Figure 4.2: Cloud point data

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Using the cloud point data, a mesh surface is

created where the iterative closest point

algorithm [7] embedded within the software

is used to interconnect adjacent points or

vertices to form a mesh of triangles. Surface

profiles are then created as illustrated in

figure 4.3.

Figure 4.3: 3D digital surface of foot sole

Figure 4.4 shows digital orthotic inserts with

geometry adapted from 3D scan data.

Figure 4.4: Customized foot orthotic insoles

5. FOOT MEASUREMENT AND

DATA COLLECTION

To make analysis on the correlation between

manually measured (MM) and computer

generated measurements (CG), foot

specimens of varying geometry for are each

successive scan cycles. Raw MM and CG

measurement data is collected and analyzed

using statistical tools. MM measurements

are obtained by using a pair of digital

vernier calipers; CG data is obtained by

using the measuring tool available in the

post-processing software (3D Reshaper). A

total of six linear foot sole dimensions that

can be used to describe the geometry of the

foot plantar surface [7] are taken and

illustrated in figure 5.1, descriptions as

follows:

i. Foot length – This is the lateral

distance along the X-axis from the

pternion (point A) to the tip of the

longest toe.

ii. Mid-foot length – The distance along

the X axis, from the pternion to the tip

along the line located at 50% of the

foot width

iii. Foot width – The maximum vertical

breadth along the Y axis and

perpendicular to the line describing the

mid foot length.

iv. Mid-foot width – Width at 50% foot

length

v. Heel to 1st toe length – Distance along

the X axis from the pternion to the

anterior 1st toe tip.

vi. Arch length – Distance along the X

axis from the pternion to the most

medially protruding point on the

metatarsal head.

1

2

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Figure 5.1: Anatomical points used for foot

measurement

5.1 Computer Generated

Measurements

To obtain most precise coordinates of start

and end points for computer generated

measurements (CG), an average of the

maximum and minimum limits of

coordinates of 5 cloud points (figure 5.2)

near to the region where the lateral and

longitudinal mid-foot line are used.

Figure 5.2: Selection of anatomical points from cloud

point data.

The lengths and are taken as the absolute

differences between the mean maximum Y

values and the mean minimum Y values

according to equation 5-1.

(5-1)

Similarly widths are taken as the absolute

differences between the mean maximum and

minimum Z values according to equation 5-

2.

(5-2)

The mean X and Z values are derived by

taking the average coordinates of 5 points

located on the cloud edge as shown in figure

5.3. All measurements are in mm.

Selection of 5 cloud

points at the cloud edge

and near the mid-foot line

Mid-foot longitudinal line

(y axis) Cloud points

representing the

scanned foot surface

i

ii iii iv

v

vi

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Figure 5.3: Computer generated measurement for an

infant’s foot sample

6. ANALYSIS AND DISCUSSION OF

RESULTS

The shells shown in figure 4.4 show some of

the key orthoses features such as the heel

‘cup’ (1) which has the purpose of

preventing the foot arch from collapsing,

and to improve the effectiveness of the arch

support by increasing the surface area of

contact and distributing pressure, and the

metatarsal pads (2) to redirect forefoot

plantar pressure. Standard deviation results

in table 6.3 show that MM and CG foot

width measurements, MM for the arch

length and CG for foot lengths varied by up

to 3 mm from their mean values. MM and

CG for heel to 1st toe, MM and CG for mid-

foot length, MM for mid foot width and MM

for foot length varied by an average of 2mm.

The least degree of variation occurred for

CG of mid-foot widths with an average of

1mm variations from the mean.

Table 6.1: Statistics obtained from Computer Generated measurement test (mm)

Measurement Sample 1 Sample 2 Sample

3

Sample 4 Sample

5

Sum Mean

Foot Length 51.187 53.440 54.424 55.990 58.065 273.106 54.6212

Mid-Foot Length 49.149 51.623 52.366 53.826 53.316 260.280 52.0560

Foot Width 35.992 35.739 32.933 31.129 29.450 165.243 33.0486

Mid-Foot Width 28.505 27.985 26.768 26.451 29.539 139.248 27.8496

Heel To 1st Toe 51.998 54.019 54.893 56.589 58.398 275.897 55.1794

Arch Length 25.725 28.983 30.053 30.428 30.895 146.084 29.2168

Table 6.2: Statistic obtained from Manual Measurement tests (mm)

Measurement Sample

1

Sample

2

Sample

3

Sample

4

Sample

5

Sum Mean

Foot Length 53.6 55.3 55.8 57.3 57.4 279.4 55.9

Mid-Foot Length 51.5 52.5 54.3 55.3 55.9 269.5 53.9

Foot Width 34.8 33.9 30.3 28.6 28.4 156.0 31.2

Mid-Foot Width 31.2 29.0 27.3 26.2 26.0 139.7 27.9

Heel To 1st Toe 54.2 55.8 55.7 58.1 59.8 283.6 56.7

Arch Length 23.5 27.2 28.5 29.1 31.2 139.5 27.9

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Table 6.3: Descriptive statistics obtained from MM and CG techniques (mm)

Measurement

Max Min Mean SD

MM CG MM CG MM CG MM CG

Foot Length 57.4 58.065 53.6 51.187 55.9 54.621 1.5707 2.5962

Mid-Foot Length 55.9 53.826 51.5 49.149 53.9 52.056 1.8601 1.8340

Foot Width 34.8 35.992 28.4 31.129 31.2 33.049 2.9858 2.8526

Mid-Foot Width 31.2 29.539 26.0 26.451 27.9 27.850 2.1767 1.2677

Heel To 1st Toe 59.8 58.398 54.2 51.998 56.7 55.179 2.2152 2.4442

Arch Length 31.2 30.895 23.5 25.725 27.9 29.217 2.8522 2.0754

Table 6.4: Coefficients of correlation and determination between CG and MM

Coefficient of correlation , r Coefficient of determination, r2

Foot Length 0.963 0.927

Mid-Foot Length 0.982 0.964

Foot Width 0.939 0.881

Mid-Foot Width 0.982 0.964

Heel To 1st Toe 0.961 0.923

Arch Length 0.945 0.893

Table 6.5: Differences between mean of MM and CG measurements (mm)

Measurement -

CG1

-

CG2

-

CG3

-

CG4

-

CG5

Sum Mean

Foot Length 55.880 4.693 2.440 1.456 -0.110 -2.185 6.294 1.259

Mid-Foot Length 53.900 4.751 2.277 1.534 0.074 0.584 9.220 1.844

Foot Width 31.200 -4.792 -4.539 -1.733 0.071 1.750 -9.243 -1.849

Mid-Foot Width 27.940 -0.565 -0.045 1.172 1.489 -1.599 0.452 0.090

Heel To 1st Toe 56.720 4.722 2.701 1.827 0.131 -1.678 7.703 1.541

Arch Length 27.900 2.175 -1.083 -2.153 -2.528 -2.995 -6.584 -1.317

CG measurements for the mid-foot widths

had the least variance (Table 6.3). Of all 5

foot surface samples, the MM foot widths

were varied the most (2.98mm). This is also

reflected in the CG measurements where

foot width measurements exhibited the

highest deviations (2.85mm). For mid-foot

lengths, the standard deviations for MM and

CG measurements are nearly similar at

1.86mm and 1.83mm respectively. A similar

case occurs for heel to 1st toe measurements

where the deviations are 2.21mm and

2.44mm respectively. From these

observations, it can be deduced that changes

in foot width, mid-mid foot length, foot

width and heel to 1st toe MM lengths are

reflected in CG measurements. However, for

mid-foot width and foot lengths, the

standard deviations for MM and CG

measurements do not correlate. This means

that for these particular measurements,

changes in MM cannot be readily used to

explain changes in CG; they can be

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attributed to system or measurement errors

discussed later in Chapter.

Table 6.5 lists the deviations of CG from the

mean of MM ( ) for all 6 dimensions.

Due to its high reliable factor, manual

measurements are used to test the reliability

CG measurements.

Table 6.6: SD between MM and CG measurements

Measurement Mean Max Min SD

Foot Length 1.259 4.693 -0.110 3.396

Mid-Foot

Length

1.844 4.751 0.074 3.307

Foot Width -1.849 0.071 -4.792 3.439

Mid-Foot Width 0.090 1.489 -1.599 2.184

Heel To 1st Toe 1.541 4.722 -1.678 4.525

Arch Length -1.317 2.175 -2.995 3.656

3.42

Results of table 6.6 show that average

deviations of 3.42 mm exist between the two

measurements. The mid-foot width

coefficient of correlation and coefficient of

determination are the highest and take the

values 0.982 and 0.965 (96.5%) respectively

(Table 6.4). These results show a strong

positive linear correlation exist between

simulated and manual mid-foot width

measurements. In this case, only 3.5% of the

total variation in computer generated

measurements is not due to a variation in

manual measurements. The consistency of

this measurement in comparison to the other

5 may be due to the topology of the scanner

where the angle of reflection is optimum

allowing laser to capture a higher intensity

of the reflected rays which would increase

the level of detail in the cloud point from

which CG measurements are taken. This

strongly suggests that the principle of laser

triangulation can be effective approach to

acquiring 3D foot surface data that is

accurate enough to depict dimensions of an

actual foot surface. Small changes in the

profile of the actual test specimen could not

be easily identified on the 3D surface

models due to factors such as camera

resolution and capabilities of the point

registration software. Due to systematic

errors and limitations, the 3D foot surface

models acquired from point cloud data did

not show the entire surface detail as is on the

test specimen. Despite this, the shape and

form as represented by the 3D surface model

was sufficient enough for use to design

corrective or customized insoles.

7. CONCLUSION

The work presented proposes an efficient

method of capturing three dimensional

surface data of the foot sole surface. This

report outlines the project objectives and

how they have been approached as well as

results that have been achieved. The foot

scan tests revealed that the 3D surface and

solid models acquired from raw 3D scan

data were able to describe the geometry of

the test specimens even when using basic

components. Further tests were conducted to

assess the correlation between actual

measurement and computer generated

measurements. Raw CG measurements

reveal significant degree of reliability where

most of these measurements varied in a

similar manner to MM measurement due to

tallying standard deviations. Also 21 out of a

total of 30 raw measurements showed

differences (MM-CG) of less than ±2mm

(tables 6.1 and 6.2). Secondly, for all

measurements the minimum correlation

amounted to 0.945. Immediate measures that

can be taken to improve the current 3D foot

scanning equipment could be aimed to

address the limitations described above.

Further design developments can be made to

allow the equipment to capture all foot

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surface dimensions. Further research into the

field of foot orthotics using three

dimensional foot scans can be used for other

diagnostics purposes apart from foot

deformities. For instance, analysis can be

made to study the feasibility of using similar

scanning equipment to acquire accurate

measurements infants’ foot prints and

determine whether computer generated

measurements can be used as a substitute for

birth weight when determining infants’

maturity at birth. This project in particular is

an example of how engineering practices

have been used to develop a solution and

therefore it is likely that it has an impact on

the social and economic scale. Once the

prototype is developed to suit the market

needs, it is likely that the social and

economic wellbeing on the target users will

be improved.

ACKNOWLEDGEMENT

I would like to acknowledge the guidance

and assistance that has been offered to me

by my project supervisor Mr. Suresh Gobee,

Mrs. Vickneswari A/P Durairajah and Asia

Pacific University School of Engineering

Laboratory staff.

REFERENCES

[1] Boardman, S., 2007. Practice

development: The economic case for

CAD/CAM custom foot orthoses. Podiatry

Now: pp. 40-41.

[2] Yu-Chee, L., (2012). Foot Shape

Classification Using 3D Scanning Data. 9-

12 July 2012. Langkawi, IEEE, pp. 1-6

[3] Hwang, J. T., Lee, K. & Oh, Y. T.,(

2005). Derivation of template shoe-lasts for

efficient fabrication of custom ordered shoe

lasts. Computer Aided Design, Volume 37,

Issue 12, pp. 1241-1250.

[4] Lee, Y.-C., Chao, W.-Y. & Wang, M.-

J., (2012). Foot Shape Classification Using

3D Scanning Data, 9-12 July 2012.

Langkawi, IEEE, p. 16.

[5] Lee, Y., Park, S., Jun, Y. & Choi, C.

W., (2004). A Robust Appoach to Edge

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International Journal of Advanced

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263-271.

[6] Witana, C., Xiong, S., Zhao, J. &

Goonetilleke, R., (2006). Foot

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scans: A comparison and evaluation

methods. International Journal of Industrial

Ergonomics, 36(9), pp. 789-807.

[7] Chetverikov, D., Svirko, D., Stepanov,

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