i

THREE DIMENSIONAL SURFACE RECONSTRUCTION OF

LOWER LIMB PROSTHETIC MODEL USING INFRARED SENSOR ARRAY

SITI ASMAH BTE DAUD

A thesis submitted in fulfillment of the

requirements for the award of the degree of

Doctor of Philosophy (Biomedical Engineering)

Faculty of Biosciences and Medical Engineering

Universiti Teknologi Malaysia

JANUARY 2016

iii

To my beloved husband and family

iv

ACKNOWLEDGEMENT In preparing the thesis, I was in contact with many lecturers and friends.

They have contributed in their own way towards my understanding and thoughts of

the whole research project. In particular, I wish to express my sincere appreciation

to my supervisor, Dr. Nasrul Humaimi bin Mahmood, for his encouragement,

guidance, critics, idea generations and friendship. I am also very thankful to my co-

supervisor Dr. Leow Pei Ling, Assoc. Prof. Dr. Azli bin Yahya, and Dr. Fauzan

Khairi bin Che Harun for their guidance, advices and motivation. Without their

continuous support and interest, the thesis would not have been the same as

presented here.

I was also indebted to my entire lab mates who have helped me by giving

brilliant ideas throughout the completion of the project. My sincere appreciation also

extends to all those who have provided assistance at various occasions. Their views

and tips are useful indeed.

A special thanks to my husband, father and mother for their constant support

throughout the years, specifically for supporting me in accomplishing the project,

without their help the project would not be complete.

v

ABSTRACT

This thesis addresses the development of a shape detector device using

infrared sensor to reconstruct a three-dimensional image of an object. The three-

dimension image is produced based on the object surface using image processing

technique. Conventionally, infrared sensors are used for detection of an obstacle and

distance measurement to avoid collisions. However, it is not common to use infrared

sensors to measure the size of an object. Hence, this research aims to investigate the

feasibility of infrared sensors in measuring the object dimension for three-dimension

image reconstruction. Experiments were executed to study the minimum distance

range utilising GP2D120 infrared sensor. From the experiment, the distance between

the sensor and object surface should be more than 5 cm. The scanning device

consists of the infrared sensor array was placed in a black box with the object in the

center. The scanning process required the object to turn 360 ° clockwise in an xy

plane and the resolution for z-axis is 2 mm, in order to obtain data for the image

reconstruction. Reference polygon shape models with various dimensions were used

as scanning objects in the experiments. The device scans object diameter every 2

mm in thickness, 100 mm in height, and the total time required to collect data for

each layer is 60 seconds. The reconstructed object accuracy is above 80 % based on

the comparison between a solid and printed model dimension. Four different lower

limb prosthetic models with different shapes were used as the object in the scanning

experiments. The experimental findings show that the prosthetic shapes

reconstructed with an average accuracy of 97 %. This system shows good

reproducibility where the collected data using the infrared sensor device need further

improvement so that it can be applied in medical field for orthotics and prosthetics

purpose.

vi

ABSTRAK

Kajian ini bertujuan bagi mencipta alat pengesan bentuk menggunakan sensor

inframerah bagi melakar semula tiga dimensi objek berkenaan. Permukaan tiga

dimensi objek ini dihasilkan menggunakan teknik pembinaan semula imej.

Kebiasaannya, sensor inframerah digunakan bagi mengesan halangan dan jarak bagi

mengelakkan pelanggaran. Walau bagaimanapun, ianya jarang digunakan untuk

mengukur saiz sesuatu objek. Justeru itu, kajian ini dilakukan bagi mengenal pasti

kebolehupayaan sensor inframerah untuk mengukur saiz objek bagi tujuan

pembinaan semula imej tiga dimensi. Eksperimen dijalankan bagi menentukan jarak

terdekat dengan menggunakan sensor inframerah GP2D120. Hasil kajian

menunjukkan bahawa, jarak di antara sensor inframerah dan objek haruslah melebihi

5 cm. Alat pengesanan ini terdiri daripada beberapa sensor inframerah yang

diletakkan di dalam kotak hitam bersama dengan objek yang berada di tengah kotak.

Proses pengesanan memerlukan objek untuk berpusing sebanyak 360 ° mengikut

arah jam di paksi xy dan berketepatan sebanyak 2 mm bagi tujuan pembinaan semula

imej. Beberapa bentuk poligon yang terdiri daripada pelbagai saiz digunakan di

dalam eksperimen ini. Alat ini mengesan objek bagi setiap 2 mm ketebalan, 100 mm

tinggi, dan jumlah masa yang diperlukan adalah sebanyak 60 saat bagi setiap lapisan.

Ketepatan pembinaan semula objek adalah melebihi 80 % berdasarkan kepada

perbezaan di antara rujukan dan data yang diperolehi. Empat bentuk model prostetik

dengan bentuk yang berbeza turut digunakan di dalam ujikaji ini. Hasil kajian

menunjukkan purata ketepatan imej adalah 97 %. Alat ini menunjukkan keputusan

yang baik dalam pengambilan data menggunakan sensor inframerah dan

penambahbaikan perlu dilakukan supaya ia boleh digunakan secara meluas didalam

bidang perubatan orthotik dan prostetik.

vii

TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES xi

LIST OF FIGURES xiii

LIST OF ABBREVIATION xvii

LIST OF SYMBOLS xix

LIST OF APPENDICES xxi

1 INTRODUCTION 1

1.1 Background 1

1.2 Problem Statement 3

1.3 Research Objectives 4

1.4 Scope of the Thesis 5

1.5 Significant Findings 6

1.6 Thesis Overview 6

2 BASIC CONCEPTS AND THEORIES 8

2.1 Introduction 8

2.2 Background of the Prosthetic Limb 9

viii

2.3 Current Techniques for Prosthetic Lower Limb

Fabrication 12

2.3.1 Medical Imaging 12

2.3.2 Laser Scanning 13

2.3.3 3D Digital Model 13

2.3.4 Camera 14

2.3.5 Sensors 16

2.3.6 The Accuracy of Medical Imaging Devices 16

2.4 Socket Development Process 17

2.4.1 Computer-aided Design and Computer-aided

Manufacturing (CAD/CAM) 18

2.4.2 Finite Element Analysis (FEA) 18

2.5 Image Reconstruction Method for 3D Image 19

2.5.1 Phong Illumination Theory 21

2.5.2 Analog-to-Digital Converter (ADC) 22

2.5.3 Power Interpolation Function 22

2.6 Research in Infrared Sensor Applications 23

2.7 Other Sensors used with Infrared Device 27

2.7.1 Ultrasound (US) 27

2.7.2 3D Sensor 28

2.7.3 Laser Diode 29

2.8 Comparison of The IR Sensor With Other Method 29

2.9 Summary 30

3 CONCEPTUAL DESIGN OF SHAPE DETECTOR

USING IR SENSOR ARRAY 32

3.1 Introduction 32

3.2 Infrared Sensor 34

3.3 Working Principle of The Circuit Control Board 37

3.4 Sensor Array Model 40

3.5 Device Hardware 45

ix

3.6 Shapes Detector Device Microcontroller

3.6.1 Arduino Microcontroller Algorithm

Development

3.6.2 Flow Chart

46

46

48

3.7 Summary 49

4 SHAPE DETECTOR DEVICE TESTED WITH

VARIOUS SHAPES OF A POLYGON MODEL 50

4.1 Introduction 50

4.2 Shapes Model 51

4.3 Image Processing using Matlab Software

4.3.1 IR Sensor Conversion Formula

4.3.2 Image Processing Post-processing Technique

57

57

60

4.4 Shape Detector Device Data Collections 65

4.5 3D Reconstructions for Various Object

4.5.1 3D Surface Reconstruction

4.5.2 Repeatability

4.5.3 Image Accuracy

65

66

69

72

4.6 Summary 73

5 APPLICATION ON THE LOWER LIMB PROSTHETIC

MODEL 75

5.1 Introduction 75

5.2 Lower Limb Prosthetic Models 76

5.3 3D Image Reconstruction for Lower Limb Prosthetic

Model 78

5.4 Image Analysis 82

5.5 Shape Detector Device Tested with Real Skin (Chicken

Drumstick) 88

5.6 Summary 91

6 CONCLUSION AND FUTURE WORKS 92

6.1 Conclusion 92

x

6.2 Future Works 94

REFERENCES 97

Appendices A - B 108-131

xi

LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 Results of non-matching vertices for six selected objects 15

2.2 Comparison of the devices used to reconstruct the 3D

image of lower limb prosthetic socket design with the

accuracy

17

2.3 Control points for measuring characteristics of the

GP2D120 sensor [77]

23

2.4 Comparison between traditional process, 3D camera, and

IR sensor in reconstructing image of an object

30

3.1 IR analog output voltage when different value of

capacitor installed at the VCC and GND of the IR

36

3.2 Results from the experiment in order to choose the

optimum IR sensor need to be install in the device

38

3.3 Analog output voltage collected with a different distance

between IR sensor and object surface

42

3.4 Functions of components in a shape detecting sensor

device

46

4.1 Mathematical equation to calculate volume of a polygon

shape model

53

5.1 Dimension of lower limb prosthetic model for each layer

in a diameter (mm)

77

5.2 List of output data obtained from the IR sensor after the

conversion equation is applied at each layer

84

5.3 The accuracy of 3D reconstructed models 86

xii

5.4 Comparison of an accuracy (mm) between existing

medical and sensor device

87

xiii

LIST OF FIGURES

FIGURE NO. TITLE PAGE

1.1 Lower limb prosthetic model design 2

1.2 (a) The prosthetic lower limb, (b) the 3D model image of

the amputee’s skin, and (c) a plaster casting mould of the

patient produce from the 3D model image 2

2.1 (a) Measurement of stump with liner and (b) chalk

negative cast with critical zones [34] 10

2.2 (a) Negative cast with opening into critical zones and

(b) positive cast [34] 11

2.3 (a) MRI image of a bones model and (b) using CT-Scan

in scanning lower limb prosthetic bones model [14] 12

2.4 3D modeling of the prosthetic limb [14] 14

2.5 Deformation process helps in matching the measured

point to the registered vertices, (a) the residual limb

shape after deformation is applied and (b) the actual

shape of the residual limb [38] 15

2.6 Finite element analysis that show the pressure at the

residual limb while wearing the prosthetic socket [14] 19

2.7 (a) Multiple rotating range sensors and (b) sensor system

overlapping area [11] 24

2.8 Range finding based on IR techniques [72] 25

2.9 The patterns of the ring surface plotted using

measurement results (a) 1 mm, (b) 2 mm, and (c) 4 mm

[74] 25

2.10 Experiment setup of the device [6] 26

xiv

2.11 YAIR infrared ring layout and shadowed sectors show

the sensitivity lobes [10] 28

3.1 Schematic structure of shape detector device 33

3.2 Infrared sensor GP2D120XJ00F (Optoelectronic device) 34

3.3 IR soldered with 100 µF capacitor 35

3.4 Circuit diagram of the 100 µF capacitor soldered in the

IR sensor 35

3.5 Transmitted and reflected signal from the IR sensor to

the object surface 37

3.6 Unwanted reflected signal from other sensors received by

a different sensor will cause error in a reading 38

3.7 Schematic design for control board circuit 39

3.8 Time diagram between the outputs of the Arduino from

PWM pins to the IR sensor to measured distance 40

3.9 Model array sensor dimension 41

3.10 Minimum distance between the IR sensor and object

surface and the maximum height of an object that can be

use to reconstruct 3D image 42

3.11 The arrangement of five IR’s installed in the sensor array

rotating an object model 43

3.12 Comparison collected sensor value data between turning

the sensor array and object model 44

3.13 The rotation of object model during data collection and

sensor measured distance at each point 44

3.14 Model sensor device 45

3.15 Time line diagram of the turn ON and OFF of the IR

sensor in a sensor array 47

3.16 Arduino board flow chart 48

4.1 Conversion from IR sensor data to Matlab data

acquisition arrangement 51

4.2 Various polygon shape model used in the experiment

(a) moon, (b) quatrefoil, (c) rectangle, (d) hemisphere,

(e) cylinder, and (f) ellipse (dimension in mm) 52

xv

4.3 The setup experiment in choosing the colour of the

printed model 53

4.4 IR sensor response for different colour papers 54

4.5 The condition of the reflectance signal of the IR sensor

both in rough and smooth surface of a model 55

4.6 3D printed polygon model using 3D printer 56

4.7 Printed 3D polygon model is measured to compared with

the design dimension (a) from the top view and (b) with a

polygon model in a flat position 56

4.8 GP2D120 example of output characteristics with inverse

number of distance [94] 58

4.9 Post-processing block diagram 60

4.10 Point fitting graph represent for both unsmooth (red line)

and smooth data plotted (blue line) 62

4.11 Point fitting data results before and after the algorithm

applied to a random data 65

4.12 3D reconstructions for polygon circle-flat model shapes

of an object (a) moon, (b) rectangle, and (c) hemisphere 66

4.13 3D reconstructions for polygon circle based model

shapes of an object (a) quatrefoil, (b) cylinder, and (c)

ellipse 68

4.14 Other model shape used in the experiment with different

shape and it is not a polygon shape (a) star, (b) tulip,

(c) flower, (d) trefoil, (e) curvilinear, and (f) egg shape 70

4.15 Bar graph of a polygon model and its accuracy (Polygon

model vs accuracy percentage) 73

5.1 Example of a lower limb prosthetic patient cut (a)

Model1, (b) Model2, (c) Model3, and (d) Model4 76

5.2 Dimension of lower limb prosthetic model 76

5.3 Printed lower limb prosthetic model using 3D printer 77

xvi

5.4 3D prosthetic model shape step-by-step image

reconstruction (a) 3D image of real sensor value data,

(b) smoothing algorithm applied to the data (robust

smooth and spline data), and (c) new reconstruction

image 78

5.5 Prosthetic models reconstructed image from the collected

data (a) Model1, (b) Model2, (c) Model3, and (d)

Model4 after smooth algorithm applied to the data 80

5.6 Whiskers bar graph between minimum and maximum

data at the same arch length (mm) using prosthetic model

(a) Model1, (b) Model2, (c) Model3, and (d) Model4 82

5.7 Chicken drumstick used in the test experiment (a) length

of the drumstick and (b) diameter of the drumstick 88

5.8 Experiment setup for a chicken drumstick inside the

sensor device 89

5.9 3D reconstruction of a chicken drumstick used in the

experiment is to be applied on real skin 90

6.1 Different type of IR sensors installed in the shape

detector sensor device to measure data of a model 95

xvii

LIST OF ABBREVIATION

3D - Three-Dimension

CT-Scan - Computed Tomography Scan

MRI - Magnetic Resonance Imaging

2D - Two Dimension

US - Ultrasound

LD - Laser diode

PSD - Position Sensing Detector

IR - Infrared

ToF - Time of Reflect

CAD/CAM - Computer-aided Design and Computer-aided

Manufacturing

FEA - Finite Element Analysis

ABC - American Board of Certification in Orthotics,

Prosthetics & Pedorthics

BOC - Board for Orthotics/Prosthetic Certification

AIPP - Amputee Independent Prosthesis Properties

RE - Reverse Engineering

ADC - Analog-to-Digital Converter

LED - Light emitting diode

FKCN - Fuzzy Kohonen Clustering Network

IC - Integrated circuit

PWM - Pulse Width Modulation

USB - Universal Serial Bus

ICPS - Input Capture Programming

xviii

ABS - Acrylonitrile Butadiene Styrene

.txt - Text file

PCB - Printed Circuit Board

xix

LIST OF SYMBOLS

θ - angle

α - Alpha

β - Beta

cm - centimeter

° - degree

Co - Diffusivity

D - distance

GND - ground

Hz - Heartz

Vin - Input Voltage

kPa - kilo Pascal

I - Light Intensity

µs - Light Source

MHz - Mega Heartz

µF - micro Farad

mm - millimeter

ms - milliseconds

% - percent

π - phi

η - Power of Specular Reflected Light

R - radius

Vref - Reference Voltage

µr - Reflected

xx

s - seconds

C1 - Specularity

Δθ - Step Size

µn - Surface Normal

µv - Viewing Vector

Vcc - Voltage

xxi

LIST OF APPENDICES

APPENDIX TITLE PAGE

A Author’s Publications List 108

A.1 Refereed Articles 108

A.2 Copyright Certificates 109

A.3 International Conferences 109

A.4 Awards 110

B Infrared Sensor GP2D120XJ00F Datasheet,

Schematic Diagram of An Arduino Board, Shape

Detector Device Circuit Connection, Polygon

Volume Shape Formula, IR Sensor Conversion

Equation, Arduino Control Board Algorithm, and

3D Image Reconstruction Developed Algorithm

111

B.1 Infrared Sensor GP2D120XJ00F Datasheet 111

B.2 Schematic Diagram of An Arduino Board 119

B.3 Shape Detector Device Circuit Connection 120

B.4 Polygon Volume Shape Formula 121

B.5 IR Sensor Conversion Equation 123

B.6 Arduino Control Board Algorithm 125

B.7 3D Image Reconstruction Developed Algorithm 129

1

CHAPTER 1

INTRODUCTION

1.1 Background

A three dimension (3D) image obtained from Computer Tomography (CT)

Scan, and Magnetic Resonance Imaging (MRI) are crucial in order to assist medical

diagnosis and analysis. These medical data provide good results but with high cost.

Meanwhile, by using two dimension (2D) images, several simulations can be done

for the needs of the user. However, a proper experiment analysis is required to

correct the obtained results that will cause additional cost for the test [1]. A 3D

image is very important as it gives full imaging details of the measured object which

will allow efficient evaluation.

A 3D image gives more data compared to 2D data images. Therefore,

providing 3D images for biomedical measurement is always better than having only

2D images to analyse patients’ conditions and their needs. Reconstructing a 3D

image requires the use of a sensor array that is normally consisted of more than one

sensor. A solution needs to be identified in order to reduce the number of sensors

used to obtain data in the 3D image. It will give an advantage to medical

practitioners by providing them with 3D data image, especially in orthotic and

prosthetic departments. Lower limb prosthetic, for example, requires detailed

information of a patient’s limb condition before designing a suitable and comfortable

prosthetic limb for the patient. Figure 1.1 shows an example of a lower limb

prosthetic model design.

2

Figure 1.1 Lower limb prosthetic model design

Conventionally, medical practitioners use chalk bandages to make new

design of prosthetic limb for their patients. Compared to other disability designs,

prosthetic is the most challenging research in order to properly interface between the

device and human body part to make sure that it fits and comfortable to the user [2].

Prosthetic is defined as a medical field in substituting or a replacement of a missing

limbs with an artificial substitution [3]. Basically, to reconstruct a residual limb, a

Prosthetic Technician needs to know the step from taking data to the reconstruction

of the image until the limb design is completed. This 3D reconstruction offers many

advantages, as the 3D image depends on the software and computer analysis to

produce more accurate data [4]. Figure 1.2 shows how researchers reconstructed a

3D image and finally produce a plaster mould of a residual limb.

(a)

(b)

(c)

Figure 1.2 (a) The prosthetic lower limb, (b) the 3D model image of the amputee’s

skin, and (c) a plaster casting mould of the patient produce from the 3D model image

Figure 1.2 shows the step by step reconstruction of the plaster mould based

on the prosthetic limb. In order to create the plaster based on the prosthetic limb, a

3D skin model is needed, and it is generated using various types of sensors. These

3

sensors are used and installed in the device to capture data for reconstructing the

images using the prosthetic lower limb.

Laser scanner, ultrasound (US), laser diode (LD), position sensing detector

(PSD), camera and infrared (IR) are used to reconstruct a 3D image of an object.

The sensor had its own advantages and drawbacks. The comparison between each

device can be seen based on their accuracy, resolution, cost, and time to collect data.

IR sensor has proven to be able to measure distance, and the data collected is used to

reconstruct an object’s surface.

Over the years, there have been significant improvements in the mobile robot

technology field as well as in devices implemented with infrared sensors. Infrared

sensor is one of the commonly used sensors in mobile robots. The sensor has many

applications such as to measure distance [5], avoidance system [6], and also obstacle

detection [7]. Other than that, it offers low price, easy installation, portable, and

provides a fast response.

IR sensors have been widely used in mobile robots with several functions,

and some of the researcher combines IR with other sensors to improve the results

obtained. For example, IR with the 3D camera to increase the resolution of the

image, IR combined with ultrasound helps in creating 3D images of an object [8],

and IR installed with 3D depth sensor can be used to measure the depth and distance

of an object [9].

1.2 Problem Statement

With the advancement of technology, there are a lot of devices that can be

used to reconstruct the 3D image from model surfaces. Some devices offer very

accurate data but with high cost, faster data preview but in 2D form, higher

resolution image but require a very high computational system, and also wide

sensing area but low in resolution. All the devices have their own advantages and

4

disadvantages. Infrared sensors offer low cost, small size, portable, lightweight and

require a few components in order to operate.

The problems with devices that are installed with infrared sensor in the

system are that they are highly dependent on object reflectance [10]. Transmitted

signal from the sensor is reflected when it hits the boundary of the reflected beam or

from the intensity of the reflected light [11]. The object surface is very important in

collecting a high impact of reflected signal to the sensor. If the object surface is too

rough, the transmitted signal is unable to highly reflect to the sensor receiver. Small

number of IR sensors installed in the device [15] will take more time to collect data

in a certain active area. Each sensor has their time of reflect (ToF) measured, which

will give results or data to the device [10]. With a small amount of IR sensors used,

total time required to obtain the data is longer.

In Orthotic and Prosthetic department, the development of lower limb

prosthetic socket required an expertise to reconstruct the shape of the residual limb

that takes a long time to complete the socket [12]. Some of the hospital used

advance technologies to reconstruct the 3D image of a residual limb. MRI and CT-

Scan have been used, and it requires patient to lie down at the bed that will deform

the actual shape of the limb [14, 15, 16]. Radiation from the CT-Scan will give a

negative effect to the patient [33, 34]. Other than that, the cost is too high and not

everybody can afford to use the technology. Traditional method to reconstruct the

residual limb is by using plaster casting. Problem occurred when the cast is dry it

will shrink, and the actual size of the socket is change. Patient will not be

comfortable with the socket that could cause skin irritation, tissue damage and ulcer

to the skin [2, 20].

1.3 Research Objectives

Based on the problem statement, there are three objectives in the research to

be achieved. All the objectives are listed below:

5

a. To design a system that is capable to control the function of an infrared

sensor array for prosthetic model data collection.

b. To develop an Arduino Microcontroller algorithm to control infrared

array sensor data acquisition and image reconstruction algorithm using

Matlab.

c. To analyse and evaluate the system performance based on the measured

results and the reconstructed images.

1.4 Scope of the Thesis

The scope of this research is divided into two different parts, which is

designing the device to collect data for a lower limb prosthetic model and post-

processing the information on the data analysis algorithm to reconstruct the 3D

image. Details of each scope are explained as follows:

a. Designing and implementing a controller circuit using Arduino

microcontroller for infrared array sensor and to rotate the prosthetic model.

Five infrared sensors are installed in the device with a control board that is

responsible to monitor the function of the overall system. Arduino is used as

a microcontroller in the device to harvest data from the IR sensors and

controls the rotation of the stepper motor. It also controls the switches of the

sensor since it will only turn ON one at a time to avoid any redundant

reflectance signal from other IRs.

b. Integrating the system with an actuator motor which is able to slowly bring

up the model to the sensor array to collect data for the whole body. An object

is placed at the center underneath the IR sensor array, and the data is

collected for image reconstruction purposes. In order to increase the height

of an object, a cupboard with a thickness of 2 mm has been slotted

underneath the object so that the device is able to measure data for the whole

object.

6

1.5 Significant Findings

This section describes the contribution of the research works in the field of a

3D image reconstruction for a lower limb prosthetic. Experimental investigation of

the development of the device is presented in this thesis. The major outcomes of this

thesis can be summarised as follows:

(1) To address the use of IRs installed in the device, an experimental

investigation was conducted on the principal sensor to measure the distance

of an object without any support from other sensors. Researchers added other

sensors in the device in order to obtain more accurate data for the results that

also required a correct arrangement between the sensors to avoid any

redundant signals.

(2) The IR sensor has been used widely in measuring distance, avoid collision

and to detect any obstacles located in front the device. The application of IR

to reconstruct the 3D image of a lower-limb prosthetic design has not yet

been reported. Data from the sensor could be used to reconstruct the

prosthetic lower limb with an accurate measurement. The accuracy is

calculated based on the diameter of the prosthetic model itself.

(3) A device installed with IR sensors to collect data of a prosthetic model was

introduced. Experimental investigation was conducted to the device, and the

results were used in the image reconstruction.

1.6 Thesis Overview

A review of a lower limb prosthetic socket development with the present

study is given in Chapter 2, including the application of IR sensors in measuring the

distance of an object’s surface. Then, in Chapter 3, the conceptual design of a shape

detector device is illustrated with the experimental investigation on the infrared

sensor characteristics, working principle of the control board design, sensor array

7

model, and the hardware device of the system. Meanwhile, Chapter 4 described the

shape detector device tested with various shapes of polygon models, detailed

explanation on the size of the polygon model used in the experiment, equation used

to convert the sensor value to the distance in a centimeter (cm), and the results of the

3D image reconstruction of the polygon shape are also shown. The repeatability

results of the object are discussed, and the accuracy proved the effectiveness of the

device. Next, the application on the lower limb prosthetic socket is tested in Chapter

5. Details on the prosthetic model used in the experiment are explained, image

analysis of the prosthetic with the percentage of accuracy is also shown. The same

concept is applied to a chicken drumstick to measure the accuracy of the device.

Finally, the thesis concludes in Chapter 6 with an outlook on future project

development.

97

REFERENCES

1. Collinger, J.L., Dicianno, B.E., Weber, D.J., Cui, X.T., Wang, W., Brienza,

D.M., and Boninger, M.L. Integrating Rehabilitation Engineering

Technology With Biologics. The Journal of Injury, Function and

Rehabilitation. 2011. 3(6): 148-157.

2. Colombo, G., and Filippi, S.A. New Design Paradigm for The Development

of Custom-Fit Soft Sockets for Lower Limb Prostheses. Computers in

Industry. 2010. 61(6): 513-523.

3. Mahmood, N.H., Camalil, O., and Ismail, A. Surface Reconstruction Using

Reference Model for Future Prosthetic Design. Jurnal Teknologi (Science

and Engineering). 2011. 54: 165-179.

4. Shuxian, W., and Wanhua, W. 3D Reconstruction of The Structure of A

Residual Limb for Customizing The Design of A Prosthetic Socket. Medical

Engineering &Amp; Physics. 2005. 27(1): 67-74.

5. Song, K.T., and Huang, S.Y. Mobile Robot Navigation Using Sonar

Direction Weights. Proceeding in IEEE International Conference on Control

Applications. 2-4th September 2004. Taipei, Taiwan: IEEE, 1073-1078.

6. Siti Nurmaini. Intelligent Low Cost Mobile Robot and Environmental

Classification. International Journal of Computer Applications. 2011. 35(12):

1-7.

7. Nwe, A.A., Aung, W.P., and Myint, Y.M. Software Implementation of

Obstacle Detection and Avoidance System for Wheeled Mobile Robot. World

Academy of Science, Engineering and Technology. 2008. 42(18): 527-577.

8. Omura, Y., Goto, A., and Shidara, N. Surface-Trace Feasibility For IR-Based

Position-Sensing Devices. IEEE Sensors Journal. 2009. 9(10): 1262-1269.

9. Dugan, U., Dongseok, R., and Myungjoon, K. Multiple Intensity

Differentiation for 3-D Surface Reconstruction With Mono-Vision Infrared

Proximity Array Sensor. in IEEE Sensors Journal. 2011. 11(12): 3352-3358.

98

10. Benet, G., Blanes, F., Simo, J.E., and Perez, P. Using Infrared Sensors for

Distance Measurement in Mobile Robots. Robotics and Autonomous Systems.

2002. 40(4): 255-266.

11. Park, H., Baek, S., and Lee, S. IR Sensor Array for a Mobile Robot. in

Proceeding of IEEE/ASME International Conference on Advance Intelligent

Mechatronics. 24-28th July 2005. Monterey, CA: IEEE, 928-933.

12. Nasir, I.A. Low Cost Obstacle Detection System for Wheeled Mobile Robot.

UKACC International Conference on Control 2012. 3-5th September 2012.

Cardiff, UK: IEEE, 529-533.

13. Tar, A., and Cserey, G. Object Outline and Surface-Trace Detection Using

Infrared Proximity Array. IEEE Sensors Journal. 2011. 11(10): 2486-2493.

14. Bonacini, D., Corradini, C., and Magrassi, G. 3D Digital Models

Reconstruction: Residual Limb Analysis To Improve Prosthesis Design. Body

Modeling and Crime Scene Investigations. 2007. 96-103.

15. Douglas, T., Solomonidis, S., Sandham, W., and Spence, W. Ultrasound

Imaging In Lower Limb Prosthesics. IEEE Transactions on Neural Systems

and Rehabilitation Engineering. 2002. 10(1): 11-21.

16. Portnoy, S., Yizhar, Z., Shabshin, N., Itzchak, Y., Kristal, A., Dotan-Marom,

Y., Siev-Ner, I., and Gefen, A. Internal Mechanical Conditions in The Soft

Tissues of A Residual Limb of A Trans-Tibial Amputee. Journal of

Biomechanics. 2008. 41(9): 1897-1909.

17. Pirouzi, Gh., Abu Osman, N.A., Eshraghi, A., Ali, S., Gholizadeh, H., and

Wan Abas, W.A.B. Review of the Socket Design and Interface Pressure

Measurement for Transtibial Prosthesis. The Scientific World Journal. 2014.

2014: 1-9.

18. Sewell, P., Noroozi, S., Vinney, J., and Andrews, S. Developments in The

Trans-Tibial Prosthetic Socket Fitting Process: A Review of Past and Present

Research. Prosthetics and Orthotics International. 2000. 24(2): 97-107.

19. Ebskov, L.B. Level of Lower Limb Amputation in Relation To Etiology: An

Epidemiological Study. Prosthetics and Orthotics International. 1992. 16(3):

163-167.

99

20. Adler, A.I., Boyko, E.J., Ahroni, J.H., and Smith, D.G. Lower- Extremity

Amputation in Diabetes: The Independent Effects of Peripheral Vascular

Disease, Sensory Neuropathy, and Foot Ulcers. Diabetes Care. 1999. 22(7):

1029-1035.

21. Jia, X., Zhang, M., Li, X., and Lee, W.C.C. Load Transfer Mechanics

Between Trans-Tibial Prosthetic Socket and Residual Limb-Dynamic Effects.

Journal of Biomechanics. 2004. 37(9): 1371-1377.

22. Pirouzi, Gh., Abu Osman, N.A., Eshraghi, A., Ali, S., Gholizadeh, H., and

Wan Abas, W.A.B. Review of the Socket Design and Interface Pressure

Measurement for Transtibial Prosthesis. The Scientific World Journal. 2014.

2014: 1-9.

23. Bader, D., Bouten, C., Colin, D., and Oomens, C.W.J. (2010). Pressure Ulcer

Research: Current and Future Perspectives. Berlin, Germany: Springer.

24. Kapp, S., and Miller, J.A. (2009) Lower Limb Prosthetics. Care of the

Combat Amputee: 553-580.

25. Chow, A., Mayer, E.K., Darzi, A.W., and Athanasiou, T. Patient-Reported

Outcome Measures: The Importance of Patient Satisfaction in Surgery.

Surgery. 2009. 146(3): 435-443.

26. Major, M.J, Twiste, M., Kenney, L.P.J., and Howard, D. Amputee

Independent Prosthesis Properties – A New Model for Description and

Measurement. Journal of Biomechanics. 2011. 44(14): 2572-2575.

27. Li, W., Liu, X.D., Cai, Z.B., Zheng, J., and Zhou, Z.R. Effect of Prosthetic

Socks On The Frictional Properties of Residual Limb Skin. Wear. 2011.

271(11): 2804-2811.

28. Lyon, C.C., Kulkarni, J., Zimerson, E., Van Ross, E., and Beck, M.H. Skin

Disorders In Amputees. Journal of Amputee Academic Dermatologic. 2000.

42(3): 501-507.

29. Mak, F.T., Zhang, M., David, A., and Boone, C.P. State-of-The-Art Research

in Lower-Limb Prosthetic Biomechanics Socket Interface: A Review.

Journal of Rehabilitation Research and Development. 2001. 38(2): 161-174.

30. Li, W., Pang, Q., Lu, M., Liu, Y., and Zhou, Z.R. Rehabilitation and

Adaptation of Lower Limb Skin To Friction Trauma During Friction Contact.

Wear. 2015. 332–333: 725-733.

100

31. Dudek, N.L., Marks, M.B., Marshall, S.C., and Chardon, J.P. Dermatologic

Conditions Associated With Use of A Lower-Extremity Prosthesis. Arch

Physics Medicine Rehabilitation. 2005. 86(4): 659-663.

32. Lee, W.C.C., and Zhang, M. Using Computational Simulation To Aid In The

Prediction of Socket Fit: A Preliminary Study. Medical Engineering Physics.

2007. 29(8): 923-929.

33. Frillici, F.S., Rissone, P., Rizzi, C., and Rotini, F. The Role of Simulation

Tools to Innovate the Prosthesis Socket Design Process. Intelligent

Production Machines and Systems. 2008. 214-219.

34. Cugini, U., Bertetti, M., Bonacini, D., Colombo, G., Corradini, C., and

Magrassi, G. Innovative Implementation In Socket Design: Digital Models

To Customize The Product. Proceedings Art Abilitation. 2006. 1: 54-61.

35. Barra, V., Morio, B., Colin, A., Vermorel, M., and Boire, J.Y. Automatic

Muscle/Fat Quantification On MR Images Of The Tight. Proceeding in 18th

Annual International Conference of the IEEE Engineering in Medicine and

Biology Society, IEEE. 31st October – 3rd November 1996. Amsterdam, the

Netherlands: IEEE, 1096-1097.

36. Colin, A., Erbland, E., Datin, C., Boire, J.Y., Veyre, A., and Zanca, M.

Automatic Muscle/Fat Quantification On MR Images. Proceeding in 17th

Anniversary Conference Engineering in Medicine and Biology, IEEE. 20-25th

September 1995. Montreal, Que, Canada: IEEE, 479-480.

37. Simmons, A., Barker, G.J., Tofts, P.S., Cluckie, A.J., Arridge, S.R., and

Coakley, A.J. Measurement Of Fat, Muscle And Bone In Human Legs. in

Proceeding Society Magnetic Resonance. 1994. 1: 253.

38. Mahmood, N.H., Omar, C., and Tjahjadi, T. Multiview Reconstruction for

Prosthetic Design. The International Arab Journal of Information

Technology. 2012. 9(1): 49-55.

39. Shin, D., and Tjahjadi, T. 3D Object Reconstruction From Multiple Views in

Approximate Circular Motion. in Proceedings of IEE SMK UK-RI Chapter

Conference on Applied Cybernetics. September 2005. Selangor, Malaysia:

70-75.

101

40. Kobayashi, T., Orendurff, M.S., Zhang, M., and Boone, D.A. Effect of

Transtibial Prosthesis Alignment Changes On Out-Of- Plane Socket Reaction

Moments During Walking in Amputees. Journal of Biomechanics. 2012.

45(15): 2603-2609.

41. Kobayashi, T., Orendurff, M.S., Zhang, M., and Boone, D.A. Effect of

Alignment Changes On Sagittal and Coronal Socket Reaction Moment

Interactions in Transtibial Prostheses. Journal of Biomechanics. 2013. 46(7):

1343-1350.

42. Boone, D.A., Kobayashi, T., Chou T.G., Arabian, A.K., Coleman, K.L.,

Orendurff, M.S., and Zhang, M. Influence of Malalignment on Socket

Reaction Moments During Gait in Amputees With Transtibial Prostheses.

Gait and Posture. 2013. 37(4): 620-626.

43. Wolf, S.I., Alimusaj, M., Fradet, L., Siegel, J., and Braatz, F. Pressure

Characteristics at The Stump/Socket Interface in Transtibial Amputees Using

an Adaptive Prosthetic Foot. Clinical Biomechanics. 2009. 24(10): 860-865.

44. Abu Osman, N.A., Spence, W.D., Solomonidis, S.E., Paul, J.P., and Weir,

A.M. Transducers for The Determination of The Pressure and Shear Stress

Distribution At The Stump-Socket Interface of Trans-Tibial Amputees.

Proceedings of the Institution of Mechanical Engineers B: Journal of

Engineering Manufacture. 2010. 224(8): 1239-1250.

45. Boutwell, E., Stine, R., Hansen, A., Tucker, K., and Gard, S. Effect of

Prosthetic Gel Liner Thickness on Gait Biomechanics and Pressure

Distribution Within The Transtibial Socket. Journal of Rehabilitation

Research and Development. 2012. 49(2): 227-240.

46. Laszczak, P., Jiang, L., Bader, D.L., Moser, D., and Zahedi, S. Development

and Validation of A 3D-Printed Interfacial Stress Sensor

For Prosthetic Applications. Medical Engineering and Physics. 2015. 37(1):

132-137.

47. Faustini, M.C., Neptune, R.R., and Crawford, R.H. The Quasi-Static

Response of Complaint Prosthetic Sockets for Transtibial Amputees Using

Finite Element Methods. Medical Engineering & Physics. 2006. 28(2): 114-

121.

102

48. Ming, Z., and Roberts, C. Comparison Of Computational Analysis With

Clinical Measurements of Stress on Below-Knee Residual Limb in Prosthetic

Socket. Medical Engineering and Physics. 2000. 22: 607-612.

49. Lee, W.C.C., Zhang, M., Boone, D.A., and Contoyannis, B. Finite-Element

Analysis To Determine Effect of Monolimb Flexibility on Structural Strength

and Interaction Between Residual Limb and Prosthetic Socket. Journal of

Rehabilitation Research & Development. 2004. 41(6): 775-786.

50. Zachariah, S.G., Saxena, R., Fergason, J.R., and Sanders, J.E. Shape and

Volume Change in The Trans-Tibial Residuum Over The Short-Term:

Preliminary Investigation From Six Subjects. Journal of Rehabilitation

Research and Development. 2004. 41(5): 683-694.

51. Sanders, J.E., Zachariah, S.G., Jacobsen, A.K., and Fergason, J.R. Changes in

Interface Pressures and Shear Stress Over Time on Trans-Tibial Amputee

Subjects Ambulating With Prosthetic Limbs: Comparison of Diurnal and Six-

Month Differences. Journal of Biomechanics. 2005. 38(8): 1566-1573.

52. Orvatinia, M., and Heydarianasl, M. A New Method for Detection of

Continuous Infrared Radiation by Pyroelectric Detectors. Physical, Sensors

and Actuators. 2012. 174: 52-57.

53. Stuart, A.D. Some Applications of Infrared Optical Sensing. Sensor

Actuators. 1993. 1-3: 185-193.

54. Schneider, H., and Liu, H.C. (2007). Quantum Well Infrared Photodetectors:

Physics and Applications. Springer-Verlag, Berlin, Heidelberg. 175-199.

55. Heisea, H.M., Kupperb, L., and Butnivac, L.N. Attenuated Total Reflection

Mid-Infrared Spectroscopy for Clinical Chemistry Applications Using Silver

Halide Fibers. Sensor Actuators. 1998. 1-3: 84-91.

56. Shukla, A., Tiwari, R., and Kala, R. Robotic Path Planning In Static

Environment Using Hierarchical Multi-Neuron Heuristic Search and

Probability Based Fitness. Journal of Neurocomputing. 2011. 74(14-15):

2314-2335.

57. Park, H., Lee, S., and Chung, W. Obstacle Detection And Feature Extraction

Using 2.5 D Range Sensor System. Proceeding International Joint

Conference SICE-ICASE’06. 18-21st October 2006. Busan, South Korea:

IEEE, 2000-2004.

103

58. Borse, H., Dumbare, A., Gaikwad, R., and Lende, N. Mobile Robot for

Object Detection Using Image. Global Journal of Computer Science and

Technology Neural & Artificial Intelligence. 2012. 12(11): 13-14.

59. Zappi, P., Farella, E., and Benini, L. Tracking Motion Detection and Distance

With Pyroelectric IR Sensors. IEEE Sensors Journal. 2010. 10(9): 295-300.

60. Yang, B., Juo, J., and Liu, Q. A Novel Low-Cost And Small-Size Human

Tracking System With Pyroelectric Infrared Sensor Mesh Network. Infrared

Physics & Technology. 2014. 63: 147-156.

61. Kakkar, A., Chandra, S., Sood, D., Tiwari, R., and Shukla, A. Multi-robot

System Using Low-cost Infrared Sensors. International Journal of Robotics

and Automation. 2013. 2(3): 117-128.

62. Mohammad, T. Using Ultrasonic and Infrared Sensors for Distance

Measurement. International Scholarly and Scientific Research & Innovation.

2009. 3(3): 269-274.

63. Korba, L., Elgazzar, S., and Welch, T. Active Infrared Sensors for Mobile

Robots. IEEE – Transcations on Instrumentation and Measurements. 1994.

2(43): 283-287.

64. Vaz, P., Ferreira, R., Grossmann, V., Ribeiro, M., Norte, I., and Pais, A.

Docking Of A Mobile Platform Based On Infrared Sensors. in Proceeding of

the IEEE International Symposium on Industrial Electronics, ISIE’97. 7-11th

July 1997. Guimaraes, Portugal: IEEE, 735-740.

65. Djurovic, Z.M., Kovacevic, B.D., and Dikic, G.D. Target Tracking With Two

Passive Infrared Non-Imaging Sensors. IET Signal Processing. 2009. 3(3):

177-188.

66. Do, Y., and Kim, J. Infrared Range Sensor Array for 3D Sensing in Robotic

Applications. International Journal of Advanced Robotic Systems. 2013. 10:

1-9.

67. Bloss, R. Simultaneous Sensing of Location and Mapping for Autonomous

Robots. Sensor Review. 2008. 28(2): 102-107.

68. Jenkin, M., Dymond, P., and Wang, H. Graph Exploration With Robot

Swarms. International Journal of Intelligent Computing and Cybernetics.

2009. 2(4): 818-845.

104

69. Sheng, W., Yangb, Q., Tan, J., and Xi, N. Distributed Multi-Robot

Coordination in Area Exploration. Robotics and Autonomous Systems. 2006.

54(12): 945–955.

70. Kim, S., Lee, S., Kim, S., and Lee, J. Object Tracking of Mobile Robot using

Moving Colour and Shape Information for The Aged Walking. International

Journal of Advanced Science and Technology. 2009. 3: 59-68.

71. Jung, S.M., Song, T.H., Park, J.H., Park J.H., and Jeon, J.W. Obstacle

Collision Prevention Using a Virtual Stick and Vibration. Proceedings of the

2008 IEEE International Conference on Information and Automation. 20-23rd

June 2008. Zhangjiajie, China: IEEE, 960-965.

72. Changhong, F., Shunxiang, W., Zhifeng, L., Xu, F., and Fanling, M.

Research and Design of The Differential Autonomous Mobile Robot Based

on Multi-Sensor Information Fusion Technology. in International Conference

on Information Engineering and Computer Science, ICIECS 2009. 19-20th

December 2009. Wuhan, China: IEEE, 1-4.

73. Lee G., and Chong, N.Y. Low-Cost Dual Rotating Infrared Sensor for Mobile

Robot Swarm Applications. IEEE Transactions on Industrial Informatics.

2011. 7(2): 277-286.

74. Tsai, C.F.,Wu, Y.K., Li, L.T., and Chen, J.C. Implementation and Analysis of

Range-Finding Based on Infrared Techniques. Proceeding of 8th Asian

Control Conference (ASCC) 2011. 15-18th May 2011. Kaohsiung, Taiwan:

IEEE, 956-959.

75. Yuzbasioglu, C., and Barshan, B. A New Method for Range Estimation

Using Simple Infrared Sensors. International Conference on Intelligent

Robots and Systems, (IROS 2005). 2-6th August 2005. Alberta, Canada: IEEE,

1066-1071.

76. Kumpakeaw, S. Twin Low-Cost Infrared Range Finders for Detecting

Obstacles Using In Mobile Platforms. in IEEE International Conference on

Robotics and Biomimetic (ROBIO) 2012. 11-14th December 2012.

Guangzhou, China: IEEE, 1996-1999.

77. Krys, V., Kot, T., Babjak, J., and Mostyn, V. Testing And Calibration Of IR

Proximity Sensors. Acta Mechanica Slovaca. 2008. 3: 1-6.

105

78. Pavlov, V., Ruser, H., and Horn, M. Feature Extraction from an Infrared

Sensor Array for Localization and Surface Recognition of Moving

Cylindrical Objects. Instrumentation and Measurement Technology

Conference, IMTC 2007. 1-3rd May 2007. Warsaw, Poland: IEEE, 1-6.

79. Garcia, M.A., and Solanas, A. Estimation of Distance to Planar Surfaces and

Type of Material with Infrared Sensors. Proceedings of the 17th International

Conference on Pattern Recognition ICPR2014. 23-26th August2004.

Cambridge, England, UK: IEEE, 745-748.

80. Yong, Q., Jing, H., and Lijie, W. Measuring System for Mobile Robot Based

on Multiple Sensors. International Conference on Electronic & Mechanical

Engineering and Information Technology. 12-14th August 2011. Harbin,

Heilongjiang, China: IEEE, 980-983.

81. Hoffmann, F. Soft computing techniques for the design of mobile robot

behaviors. Information Science. 2000. 122(2): 241-258.

82. Kumari, C.L. Building Algorithm For Obstacle Detection and Avoidance

System for Wheeled Mobile Robot. Global Journal of Researchers in

Engineering Electrical and Electronics Engineering. 2012. 12(11): 11-14.

83. Kim, S., Park, C., Lee, H., and Lee, J. Trajectory Planning of Autonomous

Robot Using Advanced Fuzzy Controller. Proceeding in IEEE International

Conference Information and Automation. 20-23rd June 2010. Harbin, China:

IEEE, 482-485.

84. Tar, A., Koller, M., and Cserey, G. 3D Geometry Reconstruction Using Large

Infrared Proximity Array for Robotic Applications. IEEE International

Conference on Mechatronics, 2009. ICM 2009. 14-17th April 2009. Málaga,

Spain: IEEE, 1-6.

85. Choset, H., Nagatani, K., and Lazar, N.A. The Arc-Transversal Median

Algorithm: A Geometric Approach to Increasing Ultrasonic Sensor Azimuth

Accuracy. IEEE Transactions on Robotics and Automation. 2003. 19(3): 513-

522.

86. Kuratli, C., and Huang, Q. A CMOS Ultrasound Range-Finder Microsystem.

IEEE Journal of Solid-State Circuits. 2000. 35(12): 180-181.

87. LeMaster, D., Karch, B., and Javidi, B. Mid-Wave Infrared 3D Integral

Imaging At Long Range. Journal of Display Technology. 2013. 9(7): 545-

551.

106

88. Murray, D., and Little, J. Using Real-Time Stereo Vision for Mobile Robot

Navigation. Autonomous Robots. 2000. 8(2): 161-171.

89. Oike, Y., Ikeda, M., and Asada, K. A 375 × 365 High-Speed 3-D Range

Finding Image Sensor Using Row Parallel Search Architecture and

Multisampling Technique. IEEE Journal of Solid-State Circuits. 2005. 40(2):

444-453.

90. Guivant, J., Nebot, E., and Baiker, S. Localization and Map Building Using

Laser Range Sensors in Outdoor Applications. Journal of Robotic Systems.

2000. 17(10): 565-583.

91. Norgia, M., Giuliani, G., and Donati, S. Absolute Distance Measurement

With Improved Accuracy Using Laser Diode Self-Mixing Interferometry in a

Closed Loop. IEEE Transactions on Instrumentation and Measurement.

2007. 56(5): 1894-1900.

92. Kawata, H., Ohya, A., Yuta, S., Santosh, W., and Mori, T. Development of

Ultra-Small Lightweight Optical Range Sensor System. IEEE/RSJ

International Conference on Intelligent Robots and Systems, (IROS 2005). 2-

6th August 2005. 1078-1083

93. Iske, B., Jager, B., and Ruckert, U. A Ray-Tracing Approach for Simulating

Recognition Abilities of Active Infrared Sensor Arrays. IEEE Sensors

Journal. 2004. 4(2): 237-247.

94. “SHARP GP2D120XJ00F Analog Output Type Distance Measuring Sensor”,

Datasheet.

95. Petrellis, N., Konofaos, N., and Alexiou, G.Ph. Target Localization Utilizing

the Success Rate in Infrared Pattern Recognition. IEEE Sensors Journal.

2006. 6(5): 1355-1364.

96. Khraisat, Y.S.H. Design Infrared Radar System. Contemporary Engineering

Sciences. 2012. 5(3): 111-117.

97. Chen, E.C., Shih, C.Y., Dai, M.Z., Yeh, H.C., Chao, Y.C., Meng, H.F., Zan,

H.W., Liu, W.R., Chiu, Y.C., Yeh, Y.T., Sun, C.J., Horng, S.F., and Hsu,

C.S. Polymer Infrared Proximity Sensor Array. IEEE Transactions on

Electron Devices. 2011. 58(4): 1215-1220.

98. Serrano, J.R., Guardiola, C., Dolz, V., López, M.A., and Bouffaud, F. Study

of The Turbocharger Shaft Motion By Means of Infrared Sensors.

Mechanical Systems and Signal Processing. 2015. 56-57: 246-258.

107

99. Rusch, P., and Harig, R. 3-D Reconstruction of Gas Clouds by Scanning

Imaging IR Spectroscopy and Tomography. IEEE Sensors Journal. 2010.

10(3): 599-603.

100. Nath, B., and Bhuiyan, A. A Geometrical Feature Based Sensor Fusion

Model of GPR and IR for The Detection and Classification of Anti-Personnel

Mines. Seventh International Conference on Intelligent Systems Design and

Applications, 2007. ISDA 2007. 20-24th October 2007. Rio de Janeiro, Brazil:

IEEE, 849-856.

101. “SD02B 2A Stepper Motor Driver.” Cytron Technologies, Johor, Malaysia.

Datasheet.

102. “Arduino Uno.” Cytron Technologies, Johor, Malaysia. Datasheet.

103. Al-Bassam, F.A. The Near Infrared Reflective Materials Surfaces:

Dimensions and Angles. Journal of University of Anbar for Pure Science.

2009. 3(2).