Automated Shoe Last Customization using MATLAB Algorithm

T. Y. Pang

1 a

, K. H. Soh

, S. Ryan

and P. Dabnichki

School of Engineering, RMIT University, Bundoora Campus East, Bundoora VIC 3083, Australia

Glenrowan Enterprises T/A GoodFit Feet Sizing, 9 De Laeter Way, Technology Park, Bantley, WA 6073, Australia

Keywords: Shoe Last, Customization, Cycling, Automated, Algorithm, CAD.

Abstract: Footwear plays an essential role in human daily life as properly fitting and comfortable footwear will

significantly improve human lives and productivity. Footwear customisation techniques aim to manufacture

footwear that fits an individual’s foot geometric. Footwear that exactly fits a person’s foot geometric will

provide more support and reduce impact when walking or when doing other activities. A customised shoe last

is an important tool used by shoemakers in manufacturing customised shoes. Currently, most customised shoe

lasts are made from the moulds of clients’ feet and all the measurements are done manually, which is a tedious

and time-consuming process. This project aims to develop a novel MATLAB (2017) algorithm that will

shorten the shoe last customization process and do so with higher accuracy. This MATLAB algorithm can

reconstruct the foot model to smooth the surface texture and rearrange the three-dimensional (3D) model

vertices for easier dimension calculations. It can also locate makers on first and fifth metatarsophalangeal

joint automatically for more accurate shoe last design. The shoe last developed using the novel algorithm was

used to create its equivalent negative moulds for the manufacturing of carbon fibre cycling shoes. The negative

moulds were 3D printed and used to produce a prototype of cycling shoes. Future research needs to consider

developing an automated algorithm to create negative moulds to speed up the cycling shoe manufacturing

process.

1 INTRODUCTION

Footwear is necessary in human daily life, as it is

designed to protect the foot from external pressure

and improve walking and sports performance. Well-

fitting footwear is important to provide support and

enhance user comfort. Ill-fitting footwear can cause

injuries and foot shape deformation, which will

reduce gait quality (Terrier et al., 2009). Finding the

right pair of shoes with the correct fit is important for

athletes, particularly with the emergence of

competitive sports. Therefore, footwear must not only

be designed to fit the users properly and to improve

comfort, but also maximize athletic performance and

minimize injury (Luximon et al., 2009, Werd et al.,

2010).

Footwear customization is an essential aspect of

manufacturing footwear that fits an individual’s feet

geometrics and dimensions to improve fit and user

comfort (Davia et al., 2013). The shoe last is an

important tool that determines the design, shape, size

and, more importantly, the fit of the final product

https://orcid.org/0000-0002-4766-3042

during the footwear manufacturing process. The

production speed of shoe last is very important in

shoemaking industry, in order to decrease the overall

shoe production budget in terms of time and money

(Zhang et al., 2012).

There are two main methods in manufacturing

customised shoe lasts: (i) the traditional method; and

(ii) a computer-based method (Telfer et al., 2010). In

the traditional footwear manufacturing method, shoe

makers use plaster to form a client’s foot geometric

in a mould. Then, footwear will be made according to

the mould (Figure 1), normally known as shoe last.

The manufacturing of a shoe last through the

traditional method is done manually and through a

trial-and-error approach that is a purely artisanal and

based on the shoemaker’s experience to fit specific

feet dimensions. It is an arduous and complex process

that takes a lot of time to manufacture due to

constraints imposed by the manual measuring of

several feet’s dimensions (Leng et al., 2006).

Nowadays, with an increasing cost of labour even

in developing countries, there is great interest in

Pang, T., Soh, K., Ryan, S. and Dabnichki, P.

Automated Shoe Last Customization using MATLAB Algorithm.

DOI: 10.5220/0007949301170122

In Proceedings of the 7th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2019), pages 117-122

ISBN: 978-989-758-383-4

117

manufacturing and design automation in the footwear

industry. The footwear customisation process can

become a lot easier when a computer-aided design

(CAD) system is introduced into this process.

Shoemakers start by using a three-dimensional (3D)

camera to scan the plaster last and user’s feet (Figure

2) and then import the scan into CAD software for

further editing and processing (Weisedel, 2007). This

CAD modelling technique can speed up the

prototyping process of customising shoe lasts, which

saves manufacturing time and money (Jimeno-

Morenilla et al., 2013). 3D scanning and CAD

techniques can also recognise landmark positions on

a foot scan accurately for more reliable measurement

results, with less than a 2mm error (Luo, 2010).

However, due to the complexity of the data, the 3D

foot scan model post-processing and analysis process

can be tedious and time-consuming. Significant error

might occur during the analysis process because of

low consistency. After the 3D data has been post-

processed, shoe lasts will be produced through either

the additive manufacturing processes, such as 3D

printing, or the subtractive manufacturing processes

such as computer numerical control machining.

Figure 1: Plaster foot mould sample.

Cycling shoes, like many other footwear, has seen

a paradigm shift in its design and manufacturing

processes. Some manufacturers are now offering

custom-tailored and handcrafted cycling shoes to fit a

specific person’s feet. For instance, Simmons Racing

(Simmons Racing©, 2019) offers fully customised

cycling shoes made out of carbon fibre, but its process

requires manual casting of a mould, which is very

time consuming and costs about US $2000 a pair.

The cycling shoe is unique when compared with

other athletic shoes. The sole of the cycling shoe

serves as the rigid link between the foot and pedal,

whereas the pedal serves as a link between shoe sole

and crank arm of the bike (Werd and Knight, 2010).

Hence, cycling shoes normally have a stiff or rigid

sole. According to Langer (Langer, 2010), cyclists try

to fit their cycling shoes as snugly as possible to

minimize any motion of the foot inside the shoe in

order to maximize energy transfer to the foot-shoe-

crank arm interface. The foot arch, heel cup and toe

box structures are the three most important parts to

consider when designing a pair of customized cycling

shoes. These are the parts that are required to fit the

user’s feet perfectly to achieve maximum support and

minimum side-to-side pressure in the toe box area.

Figure 2: 3D scanned image of a user foot from plaster last.

Our project aims to develop an automated 3D

design algorithm to create a customised cycling shoe

last from the digital data scanned from an individual's

foot shape. It is envisaged that this novel algorithm

will reduce the time required for the post-processing

phase and improve consistency and accuracy when

reconstructing the raw scanned data. A reconstruction

method is applied in this algorithm because it can

repair holes in the foot model and smooth the surface

texture to reduce noise. The algorithm will speed up

manufacturing by casting a user's foot as a mould for

a bespoke shoe last to manufacture customised

cycling shoes.

2 METHODOLOGY

The detailed design process of the algorithm

foundation is presented in this section. It is separated

into (1) foot data collection, (2) post-processing, and

(3) the raw data sectioning concept development and

MATLAB code development phase.

2.1 Foot Data Collection

All 3D foot scanned data used in this project are

collected using an INFOOT 3D foot scanner (Figure

3(a)). Five markers were placed on a user’s feet

before the scanning process, as shown in Figure 3(b).

The INFOOT scanner produced the raw scanned data

as a binary CADfix Geometry Database File (FBD)

that includes the position of markers.

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Figure 3: (a) Infoot scanner [I-Ware Laboratory Co., Ltd],

(b) Landmark locations.

2.2 Post-processing and Intersection

Foot Model Reconstruction

The raw scanned data were post-processed to remove

noise and unwanted parts, as well as to patch holes

using a CAD software. Figure 4 shows the raw

scanned data and the cleaned foot model after post-

processing in Standard Tessellation Language (STL)

format.

Figure 4: (a) Raw scanned data, (b) cleaned foot scan model

after post-processing.

2.3 MATLAB Script Development

MATLAB was used to develop an automated

algorithm that expressed matrix and array

mathematics directly. The flowchart in Figure 5

describes the brief MATLAB algorithm development

process.

Figure 5: MATLAB algorithm development process

flowchart.

The foot scan data was converted into a vertices

and faces matrix when imported into MATLAB by

using stlTools. Figure 6 shows the imported foot

model in MATLAB. All the vertices are connected by

faces triangles, which are presented in red, while blue

lines are the edges that form triangle faces.

Figure 6: 3D scanned foot model imported in MATLAB.

2.3.1 Determining the Position of the 3D

Foot Model

After the scanned foot data was imported into the

MATLAB environment, the first step was to detect

the pointing direction of the foot’s scanned data. Due

to the different system used to scan the user’s foot,

the position of foot models was not always placed on

x-axis; rather, they were randomly positioned on of

either positive or negative x and y axis. After

ascertaining the position of the foot model, the

algorithm needed to relocate the foot model to a fixed

positive x direction for the calculation of landmark

position.

2.3.2 Reconstructing the Foot Model

After that, the foot model needed to be reconstructed

to rearrange the vertices points of the foot model. A

foot sectioning needed to be considered before

developing the analysis algorithm to ensure that the

sectioning was clear and feasible. The sectioning

process was designed to cut out the unused part of the

foot scan model. Figure 7(a) shows the foot model

that has been sectioned at most lateral malleolus

height.

Figure 7: (a) sectioned foot model, (b) Intersection concept

testing on foot model.

The MATLAB code converted the 3D foot

scanned models from STL format into a ‘vertices’ and

‘faces’ matrix structure. Vertices express the 3-

dimensional coordination points of the foot model

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and faces are the triangles connecting each vertex.

The vertices from the imported foot model are usually

random and messy, so an intersection method was

needed to rebuild the foot model to generate uniform

vertices for higher accuracy in the analysis of results

(Figure 7(b)).

2.3.3 Calculating the Landmark Location

The landmark position that was calculated in this

algorithm was the first Metatarsophalangeal (MTP)

joint, which joins the head of first metatarsal and

proximal phalanx of big toe. The reason that

approximating first MTP joint position needs to be

specifically calculated is for toe box development.

The first MTP joint is always the most prominent part

of the forefoot region for the general population.

Figure 8 shows the calculated landmark position on a

foot model.

Figure 8: Calculated first MTP joint landmark position on

foot model.

The red dot on the figure above shows the calculated

first MTP joint position on the foot model. The landmark

was used to separate the foot model into rearfoot and

forefoot sections. The separation was done by calculating

the instep and fibula instep length and separating the foot

from the cross section of these two points. This was done

because the customised shoe last required the geometric of

the rearfoot (e.g. foot arch and heel) and toe box. The

examples of separated forefoot and rearfoot parts are shown

in Figure 9.

Figure 9: Forefoot and rearfoot areas.

3 RESULTS AND DISCUSSION

3.1 MATLAB Algorithm

The novel algorithm we employed processed the 3D

foot scanned models in STL format by reconstructing

and calculating the landmark positions on the foot

model using the mathematical algorithm. The first

step of the algorithm detected the normal direction of

the imported foot model, then relocated it to the

positive plane for easier calculation.

The original scanned foot model was compared

with the reconstrued shoe last model constructed by

the MATLAB algorithm shown in Figure 10. It was

clear that the forefoot and rearfoot parts had a

different number of vertices points. This occurs

because a customised shoe last model keeps the

detailed geometrical shape of heel and arch parts, so

it needs more vertices points to provide the

information. In terms of the 3D model reconstruction,

it not only rearranges the vertices points but also

rebuilds the faces that link all the vertices points

together to form a closed 3D profile. This action was

done by a boundary function in MATLAB. The

boundary function can generate faces that connect all

the reconstructed vertices to form a 3D object, and the

details of the generated faces can also be controlled

by changing the boundary function coefficient

number. However, the boundary function cannot

completely cover every detail of the reconstructed

model. Holes will appear in some foot model data due

to the complexity of the input model and some

geometric information will disappear due to the

boundary error.

Figure 10: Reconstructed shoe last model, Original (left)

and result (right) of the 3D foot model.

An overlapped comparison between the original

and reconstructed model is shown in Figure 11. The

red part refers to the original foot model and the blue

part is the reconstructed model. The boundary error

can be minimised by reducing the number of

intersections produced during the reconstruction

phase.

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Figure 11: Overlapped comparison of original (red) and

reconstructed foot model (blue).

3.2 Shoe-last Customization and

Prototyping

The intention was to use the shoe last as a mould to

produce a pair of cycling shoe prototypes using

composites materials. The reconstructed foot model

was then split into two parts: the top and the bottom

parts (Figure 12).

Figure 12: The reconstructed foot model was sectioned into

two parts: top and bottom parts.

The top and bottom parts was then converted into

negative male and female moulds (Figure 13) for

manufacturing of carbon fibre cycling shoes through

composite layering.

Figure 13: Male and female mould for carbon fibre

manufacturing processes.

The output products of this algorithm were 3D

printed using Acrylonitrile Butadiene Styrene (ABS)

3D filament for prototype testing purposes. During

the manufacturing process, a multi-layer of composite

fiber materials was laid over the negative male and

females shoe last moulds. The moulds and composite

fibre materials were then placed into a vacuum bag to

shape the materials according to the shoe last’s design

(Figure 14).

Figure 14: 3D printed male and female moulds, and

porotype of carbon fibre cycling shoes.

4 CONCLUSIONS

The MATLAB algorithm developed in this project

successfully designed a shoe last from a raw STL file

automatically, regardless of the normal direction of

the original input model. It cleaned up the original

scan file by reconstructing the vertices of the 3D

model, which allowed further calculations. In other

words, the algorithm did significantly shorten the

shoe last customisation process to under two seconds,

as everything was done automatically by the

MATLAB algorithm.

The output shoe last model had an approximate

+/-2mm error difference from the original foot model,

which was deemed an acceptable result for accurate

shoe last design. Future development of this

algorithm will focus on improving the boundary

function and creating a better resolution of the

enclosed surface.

The output shoe last was converted into a negative

male and female moulds, 3D printed and tested by

using it in the actual shoe manufacturing process. We

created negative moulds based on the shoe last

developed by the novel customization algorithm, and

the attempt at producing a composite cycling shoe

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was successful. Future work in this project will focus

on automating the negative mould development

processes.

ACKNOWLEDGEMENTS

The authors would like to thank the technical staff

from RMIT Advanced Manufacturing Precinct for

their help in 3D printing the prototypes. We would

also like to express our sincere thanks to Dr Floreanna

Coman for her valuable advice and assistance in

constructing the carbon fibre shoes.

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