ANALYZING CROSS-TALK IN
MECHANOMYOGRAPHIC SIGNALS OF FOREARM MUSCLES DURING GRIP FORCE
TASK AND DIFFERENT WRIST POSTURES
by
Md. Anamul Islam (1141310594)
A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy
School of Mechatronic Engineering UNIVERSITI MALAYSIA PERLIS
2014
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ACKNOWLEDGEMENT
In the name of Almighty ALLAH, The Most Gracious, The Most Merciful, Who blessed me to understand and perform this work within my anticipated time-frame. I would like to present my greatest and sincerest thanks to Him for giving me wisdom and fortitude during the entire course of this PhD duration.
I would like to confer my utmost gratitude to my main supervisor Assoc. Prof.
Ir. Dr. Kenneth Sundaraj, School of Mechatronic Engineering, Universiti Malasyia Perlis (UniMAP), for his generous support, outstanding guidance, motivation, wonderful suggestions, discussion and strong faith that he provided me throughout the work. His continuous inspirations for working and publishing papers, constructive criticism, and sincere willingness to solve problems have made this work to be completed within expected time.
I would also like to thanks my co-supervisor Prof. Dr. R. Badlishah Ahmad, Dean, School of Computer and Communication Engineering, UniMAP very much for his helpful support during this work.
I would like to give special thanks to UniMAP for the facilities and financial support provided to me for my PhD degree.
I like to thank Dr. Sebastian Sundaraj, Medical Officer, Malaysian Ministry of Health, Klang, Selangor, Malaysia very much for his presence to help perform the data collection for this study.
I am also grateful to all of my laboratory colleagues, AI-Rehab Research Group, UniMAP for their continuous friendly supports, which encourage me to do work attentively. I thank you all very much and proud to be part of your friends.
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I am also grateful to all the subjects for their participation in this study. The outcomes that may be contributed in practices would not be possible without their participations.
It is my fortune to gratefully acknowledge the moral support and encouragement from my family members especially my beloved mother Saleha Khatun. The words and emotions fail me to express my deepest appreciation to my beloved mother for her love and prayer. I am also grateful to my elder brother Mohidul, younger sister Shati, and nephew, Sujon, for their continuous encouragement and support during this work.
Last but not the least; I would like to thank my lovely wife, Sorna, very much for her love, prayer, understanding, encouragement and assistance in this work. Without her passion, I would not have finished this thesis. I thank my little son, Yaeesh, a great cheer to me, for playing and smiling with me, which keep tiredness away from me even after a day-long labor.
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TABLE OF CONTENTS
THESIS DECLARATION i
ACKNOWLEDGEMENTS ii
TABLE OF CONTENTS iv
LIST OF TABLES vii
LIST OF FIGURES ix
LIST OF ABBREVIATIONS xi
LIST OF SYMBOLS xiii
ABSTRAK xv
ABSTRACT xvi
CHAPTER 1 INTRODUCTION
1.1 Background 1
1.2 Problem Statement and its Significance 4
1.3 Motivation of the Research 5
1.4 Research Questions 6
1.5 Research Objectives and Hypotheses 7
1.6 Research Scope 9
1.7 Thesis Organization 11
CHAPTER 2 LITERATURE REVIEW
2.1 Overview to Human Muscle 12
2.2 Surface Mechanomyography 21
2.3 MMG Signal Recording Techniques 22
2.4 MMG and Muscle Contractions 23
2.5 Propagation Properties of MMG Signal 25
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2.6 Cross-talk in MMG Signal 26
2.7 Cross-talk Quantification Methods 28
2.8 Cross-talk and Muscle Actions 29
2.9 Cross-talk and Signal Propagation Axis 30 2.10 Cross-talk and Muscle Anthropometric Parameters 31
2.11 Summary 33
CHAPTER 3 METHODOLOGY
3.1 Participants 35
3.2 Ethics Statement 36
3.3 Muscle Contraction Protocols 36
3.3.1 Isometric Wrist Posture 37
3.3.2 Sub-maximal to Maximal Isometric Contraction 43
3.4 MMG Signal Recording 48
3.4.1 Sensor Type and its Attachment 48
3.4.2 Sensor Location over Muscle Bellies 49 3.4.3 Measurements of Anthropometric Parameters 50
3.4.4 Data Acquisition 51
3.5 MMG Signal Processing 52
3.5.1 Filtering 52
3.5.2 Feature Extraction 54
3.6 Cross-talk Quantification 55
3.7 Data Analysis 57
3.7.1 Regression Analysis 57
3.7.2 One-Way Repeated Measures Analysis of Variance 59
3.7.3 Effect Size 62
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3.7.4 Post Hoc Test 63
3.7.5 Statistical Significance Level 63
3.8 Summary 64
CHAPTER 4 RESULTS AND DISCUSSION
4.1 Cross-talk in MMG Signal Propagated from Muscle Axes 65 4.2 Cross-talk in MMG Signals during different Muscle Actions 77 4.2.1 Cross-talk in MMG Signals during the Wrist Postures 77 4.2.2 Cross-talk in MMG Signals during different Grip Forces 86 4.2.3 Cross-talk in MMG Signals due to Tremor and 90
Muscle Fibre Types
4.2.4 Cross-talk Within and Between Compartmental Muscles 95 4.3 Cross-talk and Anthropometric Parameters 96 4.3.1 Cross-talk and Circumference of the Forearm 96 4.3.2 Cross-talk and Length of the Forearm 98 4.3.3 Cross-talk and Skin-fold Thickness of Muscles 99
4.4 Summary 100
CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusions 102
5.2 Major Contributions and Significance of the Results 103
5.3 Limitations 104
5.4 Future Recommendations 105
REFERENCES 106
APPENDIX A Subject Consent Form 124
APPENDIX B Ethics Approval 126
APPENDIX C F-distribution Table 130
APPENDIX D List of Publications 133
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LIST OF TABLES
No Page
1.1 The origin, insertion and functional activity of muscles investigated. 10
3.1 Descriptive variables of the participants 36
3.2 Submaximal to maximal isometric contraction of the grip force
produced by the participants 47
3.3 Measurements of the anthropometric parameters for the forearm
muscles 50
3.4 Filter parameters used for MMG signal in this study 53 4.1 Example of the pattern of MMG signals detected by the Lo, La, and Tr
axes of the ED muscle during the WE posture for a participant 66 4.2 Example of the pattern of MMG signals detected by the Lo, La, and Tr
axes of the ECU muscle during the WE posture for a participant 67 4.3 Example of the pattern of MMG signals detected by the Lo, La, and Tr
axes of the FCU muscle during the WE posture for a participant 68 4.4 Statistical analyses of the cross-talk values among the Lo, La and Tr
axes of the ED and ECU muscles during each wrist posture 74 4.5 Statistical analyses of the cross-talk values among the Lo, La and Tr
axes of the ECU and FCU muscles during each wrist posture 75 4.6 Statistical analyses of the cross-talk values among the Lo, La and Tr
axes of the ED and FCU muscles during each wrist posture 75 4.7
LSD post hoc analyses of the cross-talk values between the ED and ECU among the Lo, La and Tr axes of the muscle groups during each wrist posture
76
4.8
LSD post hoc analyses of the cross-talk values between the ECU and FCU among the Lo, La and Tr axes of the muscle groups during each wrist posture
76
4.9
LSD post hoc analyses of the cross-talk values between the ED and FCU among the Lo, La and Tr axes of the muscle groups during each wrist posture
77 4.10 Example of the pattern of MMG signals generated by the ED muscle
during the wrist postures for a participant
79
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4.11 Example of the pattern of MMG signals generated by the ECU muscle
during the wrist postures for a participant 81
4.12 Example of the pattern of MMG signals generated by the FCU muscle
during the wrist postures for a participant 83
4.13 Analyses of the significance of the cross-talk in the MMG signals
during the wrist postures for each muscle group 85
4.14
LSD post hoc tests for the analysis of significance of the cross-talk in the MMG signals between the ECU and FCU muscles during the wrist postures
86
4.15 Example of the pattern of MMG signals generated by the a) ED, b)
ECU, and c) FCU muscles at 100% MVIC 87
4.16 Statistical analyses of the cross-talk among the MMGTF, MMGSF and
MMGFF signals between the muscle groups at 100% MVIC 92 4.17
LSD post hoc analyses of the cross-talk values among the MMGTF, MMGSF and MMGFF signals between the muscle groups at 100%
MVIC
93
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LIST OF FIGURES
No Page
2.1 Anatomy of a skeletal muscle 13
2.2 Muscle fibre arrangements, parallel and oblique 15
2.3 Anatomical diagram of a skeletal muscle with its motor unit 17 2.4 Schematic of A) anterior and B) posterior views of the superficial muscles of
the right forearm 19
2.5 Anatomy of bones in the elbow, forearm and hand of a right upper limb 20
2.6 Intrinsic muscle groups of a right hand 20
2.7a RMS amplitude of MMG signals during the submaximal isometric (cross lined
bar), concentric (gray bar) and eccentric (white bar) contractions 24 2.7b MPF of MMG signals during the submaximal isometric (cross lined bar),
concentric (gray bar) and eccentric (white bar) contractions 24
3.1 Basic block diagram of the protocol for this study 37
3.2
Schematic of an experimental setup during the UD wrist posture for one participant including accelerometers placement over the ED, ECU, and FCU muscles
38
3.3 Flow chart for the protocol of a trial of the wrist postures 39 3.4 Schematic of the wrist postures of a) WF, b) WE, c) RD, and d) UD for one
participant, which were used to obtain MMG signals 40
3.5 Protocol for contraction and resting times during the wrist postures. 6 s and 2
min are the contraction and resting times, respectively 41 3.6 Flow graph for the activity of data collection during each muscle action 42 3.7 Protocol for the determination of maximal voluntary isometric contraction
(MVIC) of the grip force 43
3.8 Protocol for submaximal to maximal isometric contractions of the grip force,
which were used to obtain MMG signal 44
3.9 Flow chart for the protocol of the % MVIC of the grip force 45
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3.10 Schematic of an experimental setup during submaximal to maximal isometric
grip force with attached accelerometers over the ED, ECU and FCU muscles 46 3.11
Orientations of the Lo, La, and Tr axes of an accelerometer with respect to muscle fibre to detect MMG signals for the quantification of cross-talk in the axes
48
3.12 Anatomical positions of the accelerometers, shown as square shape, over the
ED, ECU, and FCU muscles 49
3.13 Schematic of skin-fold measurement over marked place of the FCU muscle for
a participant 51
3.14 Experimental setting for data acquisition in this study 52 4.1
Box and whisker plots of cross-talk values in the Lo-axis MMG signals between the a) ED and ECU, b) ECU and FCU, and c) ED and FCU muscles during the wrist postures
69
4.2
Box and whisker plots of cross-talk values in the La-axis MMG signals between the a) ED and ECU, b) ECU and FCU, and c) ED and FCU muscles during the wrist postures
70
4.3
Box and whisker plots of cross-talk values in the Tr-axis MMG signals between the a) ED and ECU, b) ECU and FCU, and c) ED and FCU muscles during the wrist postures
71
4.4
Box and whisker plots of the cross-talk values in MMG signals between the a) ED and ECU, b) ECU and FCU, and c) ED and FCU muscles during the grip forces
88
4.5
Correlations between the grip force and level of the cross-talk in the MMG signals between: a) ED and ECU, b) ECU and FCU, and c) FCU and ED muscles
89
4.6
The level of cross-talk in the MMGTF, MMGSF and MMGFF signals between the a) ED and ECU, b) ECU and FCU, and c) ED and FCU muscles at 100%
MVIC
91
4.7
Correlations between circumference of the forearm and level of cross-talk between: a) ED and ECU, b) ECU and FCU, and c) FCU and ED muscle groups
97
4.8 Correlations between length of the forearm and level of cross-talk between: a)
ED and ECU, b) ECU and FCU, and c) FCU and ED muscle groups 98 4.9 Correlations between skin-fold thickness and the level of cross-talk between:
a) ED and ECU, b) ECU and FCU, and c) FCU and ED muscle groups
99
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LIST OF ABBREVIATIONS
ACC – Accelerometer
ANOVA – Analysis of Variance
CC – Contractile Component
cDAQ – Compact Data Acquisition CMMR – Common Mode Rejection Ratio dfB – Degrees of Freedom Between dfE – Degrees of Freedom Error dfP – Degrees of Freedom Participant dfT – Degrees of Freedom Total ECU – Extensor Carpi Ulnaris
ED – Extensor Digitorum
EMG – Electromyography FCU – Flexor Carpi Ulnaris
LSD – Least Significant Difference
MK – Myokinetic
MMG – Mechnomyography
MMGFF – Mechnomyographic Signal due to Fast-twitch Fibres MMGSF – Mechnomyographic Signal due to Slow-twitch Fibres MMGTF – Mechnomyographic Signal due to Force Tremor MPF – Mean Power Frequency
MREC – Medical Research & Ethics Committee
MS – Mean Squared Error
MVC – Maximum Voluntary Contraction
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MVIC – Maximum Voluntary Isometric Contraction
NI – National Instrument
PEC – Parallel Component
RC – Resistance and Capacitance
RD – Radial Deviation
RMS – Root Mean Square
SD – Standard Deviation
SE – Standard Error
SEC – Series Element of Passive Component sEMG – Surface Electromyography
SS – Sum of Squares
SSB – Sum of Squares Between
SSE – Sum of Squares Error SSP – Sum of Squares Participant SST – Sum of Squares Total SSW – Sum of Squares Within
UD – Ulnar Deviation
WE – Wrist Extension
WF – Wrist Flexion
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LIST OF SYMBOLS
cm – Centimeter
dB – Decibel
F – Calculated F-ratio
Fc – Critical F-ratio
g – Gravitational acceleration
Hz – Hertz
k – Number of group
Kg – Kilogram
KHz – Kilohertz
KΩ – Kiloohom
La – Lateral
Lo – Longitudinal
m/s2 – Meter per second square
mm – Millimeter
mV – Millivolt
N – Newton
n – Number of participant
N – Total number of observation
p – Significant value
r2 – Determination of coefficient R2xy – Level of cross-talk
Rxy – Cross-correlation coefficient
s – Second
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T – Length of a signal
Tr – Transverse
XG – Grand mean
Xg – Group mean
XP – Participant mean
y – Year
β0 – Intercept of regression line β1 – Slope of regression line
ε – Random error
η² – Effect size
– Time lag
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Analisa Cakap-Silang dalam Isyarat Mekanomiografi Otot Lengan semasa Aktiviti Genggaman Kuat dan Postur Gelang Tangan Berbeza
ABSTRAK
Dalam mekanomiografi (MMG), cakap-silang merujuk kepada pencemaran isyarat dari otot yang berkepentingan oleh isyarat dari otot lain atau kumpulan otot berdekatan.
Kajian ini menganalisa cakap-silang dalam isyarat MMG yang dihasilkan oleh otot digitorum ekstensor (ED), ekstensor karpi ulnaris (ECU), dan fleksor karpi ulnaris (FCU) semasa pengecutan isometrik submaksimal kepada maksimal genggaman kuat (MVIC) dan postur gelang tangan seperti fleksi (WF), lanjutan (WE) radial (RD) dan ulnar (UD). Dua puluh lelaki sihat bertangan kanan (min ± SD: umur = 26.25 ± 3.13 y) telah mengambil bahagian dalam kajian ini. Semasa setiap tindakan otot, tiga akselerometer mikroelektromekanikal (MEMS) tiga paksi (berskala penuh = ± 3g, sambutan frekuensi = 0.5-500 Hz, kepekaan = 330 mV/g) telah digunakan untuk mendapatkan isyarat MMG dari arah membujur (Lo), sisi (La) dan melintang (Tr) kepada serat otot. Pekali puncak-korelasi pada susulan masa sifar telah digunakan untuk pengkuantitian cakap-silang. Analisis ukuran-berulang varians (ANOVA) diikuti dengan ujian post hoc perbezaan paling signifikan (LSD) pada tahap signifikan = 0.05 telah dijalankan untuk menganalisis cakap-silang. Tahap cakap-silang dalam isyarat MMG yang dihasilkan oleh tiga paksi otot berjulat antara R2xy = 27-70% bagi paksi-Lo, 14-53% untuk paksi-La, dan 9-26% bagi paksi-Tr di mana didapati isyarat cakap-silang jauh lebih rendah pada paksi-Tr MMG untuk semua postur gelang tangan (p < 0.05).
Selain itu, postur gelang tangan, kecuali RD, tidak mempengaruhi secara ketara tahap cakap-silang antara otot (p > 0.05). Terdapat korelasi positif yang kuat antara tahap cakap-silang dan aktiviti genggaman kuat oleh otot (r2 ≥ 0.857). Cakap-silang juga terhasil di dalam isyarat MMG oleh daya menggeletar (MMGTF), serat unit motor yang lambat-menembak (MMGSF) dan cepat-menembak (MMGFF) dengan nilai cakap-silang yang bersignifikan lebih besar dan lebih kecil untuk masing-masing MMGTF dan MMGFF (p < 0.05). Terdapat korelasi positif yang lemah antara tahap cakap-silang dan lilitan lengan semasa pengaktifan isyarat MMG yang maksima (r2 ≤ 0.216). Walau bagaimanapun, terdapat hubungan korelasi negatif yang lemah antara cakap-silang dan panjang lengan (r2 ≤ 0.082) dan antara cakap-silang dan ketebalan kulit (r2 ≤ 0.30).
Keputusan ini boleh digunakan untuk meningkatkan pemahaman kita mengenai mekanisma otot-otot lengan semasa postur gelang tangan dan genggaman kuat dengan menggunakan teknik MMG.
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Analyzing Cross-Talk in Mechanomyographic Signals of Forearm Muscles during Grip Force Task and Different Wrist Postures
ABSTRACT
In mechanomyography (MMG), cross-talk refers to the contamination of the signal from the muscle of interest by a signal from another muscle or muscle group in close proximity. This study analyzed the cross-talk in MMG signals generated by the extensor digitorum (ED), extensor carpi ulnaris (ECU), and flexor carpi ulnaris (FCU) muscles during submaximal to maximal isometric contractions of the grip force (MVIC) and wrist postures of flexion (WF) and extension (WE), and radial (RD) and ulnar (UD) deviations. Twenty, healthy right-handed men (mean ± SD: age = 26.25 ± 3.13 y) participated in this study. During each muscle action, three microelectromechanical systems (MEMS)-based tri-axial accelerometers (full-scale range = ± 3g, typical frequency response = 0.5-500 Hz, sensitivity = 330 mV/g) were used to obtain the MMG signals from the longitudinal (Lo,), lateral (La) and transverse (Tr) directions with respect to muscle fibres. Peak cross-correlation coefficients at zero time lags were used for quantification of the cross-talk. Repeated-measures analysis of variance (ANOVA) followed by least significant difference (LSD) post hoc tests at a significant level = 0.05 were performed to analyze the cross-talk. The level of cross-talk in the MMG signals generated by the three axes of the muscles ranged from R2xy = 27-70% for the Lo-axis, 14-53% for the La-axis, and 9-26% for the Tr-axis providing significantly lower cross- talk in the Tr-axis MMG signals for all the wrist postures (p < 0.05). Additionally, the wrist postures, except the RD, did not significantly influence the level of cross-talk between the muscles (p > 0.05). There were strong positive correlations between the level of cross-talk and the grip forces for the muscles (r2 ≥ 0.857). The cross-talk also occurred among the MMG signals due to force tremor (MMGTF), slow-firing (MMGSF) and fast-firing (MMGFF) motor unit fibres for the muscles with significantly greater and smaller cross-talk values for the MMGTF and MMGFF signals, respectively (p < 0.05).
There were weak positive correlations between the level of cross-talk and circumference of the forearm during maximally activated MMG signals (r2 ≤ 0.216). However, there were weak negative correlations between the cross-talk and the length of forearm (r2 ≤ 0.082) and the cross-talk and muscles’ skin-fold thickness (r2 ≤ 0.30). The results may be used to improve our understanding on mechanics of the forearm muscles during the wrist postures and gripping task for using the MMG technique.
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1 CHAPTER 1
INTRODUCTION
1.1 Background
Researchers are exploring to set suitable methods to examine muscles’ activities noninvasively; these methods for example, include surface electromyogram (sEMG) (Cho & Kim, 2012; Simoneau, Longo, Seynnes, & Narici, 2012), , sonomyogram (SMG) (Chen et al., 2011; Shi, Chang, & Zheng, 2010) and mechanomyogram (Cooper, Herda, Vardiman, Gallagher, & Fry, 2014; Ibitoye, Hamzaid, Zuniga, & Abdul Wahab, 2014). Of these techniques, the sEMG, measures electrical activity from superficial muscle, has been widely accepted as a reliable tool to examine the condition of muscle function for kinesiological and rehabilitation purposes. However, the sEMG is also observed sensitive to skin impedance changes due to sweating and electrode placement over muscle, which is a problem in certain applications of wearable sensor fields such as prosthetics (Anderson, Wybo, & Bartol, 2010; Ma, 2009; Orizio, 1993). In addition, the sEMG is very sensitive to power line and external electrical noise interferences (Mercer, Bezodis, DeLion, Zachry, & Rubley, 2006; Wollaston, 1810). The sEMG is also observed susceptible to motion artefacts and thus provides very low Signal-to- Noise Ratio (SNR), which demands for a low noise and high gain acquisition system to record a useable sEMG signal (Anderson, et al., 2010; Galiana-Merino, Ruiz- Fernandez, & Martinez-Espla, 2013). Thus, researchers have been trying to find alternative to sEMG technique and discovered the surface mechanomyogram (MMG).
Several terms including soundmyography (Orizio & Veicsteinas, 1992),
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phonomyography (Guillaume, Guillaume, Denis, François, & Thomas, 2002 ; Hemmerling, Babin, & Donati, 2003), acoustic-myography (Rodriguez, Agre, Franke, Swiggum, & Curt, 1996), accelerometermyography (Lammert, Jorgensen, & Einer- Jensen, 1976), and vibromyography (Matheson et al., 1997; Mealing, Long, &
McCarthy, 1996) have been used initially to describe MMG. In 1995, the CIBA Foundation Symposium suggested a common term “surface mechanomyogram” to distinguish the MMG signal from other mechanical signals that are unrelated to muscle activity (Orizio, Gobbo, Diemont, Esposito, & Veicsteinas, 2003).
According to a research published by (Beck, 2010), the MMG was first discovered in 1663 by Francesco Grimaldi, who claimed hearing a rumbling sound while he placed his thumbs tending to his ears and clenched his fists. In 1810, William Hyde Wollaston also observed the sound signal from a muscle during contraction (Wollaston, 1810). Gordon and Holbourn in 1948 made the first step in order to record MMG signal with a microphone. Consequently, they concluded that the surface MMG signal is the mechanical counterpart of the motor unit electrical activity as measured by sEMG (Gordon & Holbourn, 1948). Due to the advancements of electronic devices and digital signal processing tools, Oster and Jaffe first ensured that MMG signal was generated by muscle activity and the amplitude of the MMG signal increased with the level of muscle activity (Oster & Jaffe, 1980).
The MMG has several potential advantages over sEMG technique. The MMG is less sensitive to sensor placement over muscle and provides clearer picture of motor unit recruitment and its firing rate for both superficial and deeper muscles (Orizio, 1993;
Orizio, et al., 2003). The MMG, due to its mechanical nature, is insensitive to skin impedance changes caused by perspiration, and is very less sensitive to electrical cross- talk between sensors caused by external noise (Barry, Leonard, Gitter, & Ball, 1986;
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Posatskiy & Chau, 2012). Additionally, since the MMG acquisition system is simpler (because it uses only one transducer compared to bipolar sEMG that uses three electrodes), this technique is very useful for muscles that are in close proximity and with limited surface area for placing recording sensors (Anderson, et al., 2010; Beck, 2010). Several studies claimed that the MMG provides higher SNR value compared to sEMG (C. Murphy, Campbell, Caulfield, Ward, & Deegan, 2008; M. Nolan, 2013; Y.
Nolan & dePaor, 2004). Particularly, Murphy et al., 2008 reported that an accelerometer based MMG signal showed better SNR than sEMG signal (C. Murphy, et al., 2008).
As a result, the MMG technique has recently been extensively used to examine the conditions of muscle functions in the clinics and rehabilitation centers. The applications for example include examination of muscle dystrophic process (Barry, Gordon, & Hinton, 1990; Orizio et al., 1997), muscle fatigue (Armstrong, 2011; Barry, Geiringer, & Ball, 1985; Cè, Rampichini, Limonta, & Esposito, 2013; Hendrix et al., 2010; Xie, Guo, & Zheng, 2010), muscle stiffness (Dobrunz, Pelletier, & McMahon, 1990; Jarocka, Marusiak, Kumorek, Jaskólska, & Jaskólski, 2012), muscle strength (Marek et al., 2005; Matta et al., 2005), muscle fibre composition (Herda et al., 2010;
Šimunic et al., 2011), exercise trainings (Cramer et al., 2007; Esposito, Limonta, & Cè, 2011; W. McKay, Vargo, Chilibeck, & Daku, 2013; W. P. McKay, Chilibeck, & Daku, 2007), neuromuscular disorders (Madeleine & Arendt-Nielsen, 2005; Marusiak, Jaskólska, Kisiel-Sajewicz, Yue, & Jaskólski, 2009; Tian, Liu, Li, Fu, & Peng, 2010) and balance (Armstrong et al., 2010). In addition, several researchers also reported that the MMG may be used in movement classification (Kawakami et al., 2012; Scheeren, Krueger-Beck, Nogueira-Neto, Nohama, & Button, 2010), prosthesis device and binary switch control (Alves, 2010; Alves & Chau, 2010; Alves, Sejdic, Sahota, & Chau, 2010;
Barry, et al., 1986; Woodward, Gardner, Angeles, Shefelbine, & Vaidyanathan, 2014),
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muscle-machine interface (M. Nolan, 2013) communication channel for disabled patients (Narayanan, Irfan, Geethanjali, & Kumar, 2012; Y. Nolan & dePaor, 2004) and monitoring neuromuscular blockade (Claudius & Viby-Mogensen, 2008; G. S. Murphy et al., 2011; Trager, Michaud, Deschamps, & Hemmerling, 2006). However, cross-talk
between adjacent muscles limits using the MMG technique for comprehensive examination of muscle function (Beck, DeFreitas, & Stock, 2010; Ebersole, Housh, Johnson, Evetovich, & Smith, 2001). Hence, the cross-talk in the MMG signals will be analyzed in this study and is elaborated in the next chapter.
1.2 Problem Statement and its Significance
As mentioned previously, the MMG technique has been applied in both clinics and rehabilitation centers. By this time, many investigators have addressed some technical factors such as sensor type (Beck et al., 2006), sensor orientation and fixation over muscle (Barry, 1987; Bolton, Parkes, Thompson, Clark, & Sterne, 1989; Farina, Li,
& Madeleine, 2008), sensor weight (Watakabe, Mita, Akataki, & Ito, 2003), contact pressure between the sensor and muscle (Bolton, et al., 1989), sensor location on muscle (Alves, et al., 2010; Zuniga et al., 2010), and temperature effect on muscle’s mechanical properties (W. McKay, et al., 2013; Orizio, 1993). These studies have put great practical importance for establishing the validity of MMG technique.
However, many questions regarding MMG signal contamination still remains to be answered to use the technique for a comprehensive examination of muscle function.
For example, the cross-talk that occurs between adjacent muscles is one of the more important concerns associated with both MMG (Beck, et al., 2010; Ebersole, et al., 2001) and sEMG techniques (Hagg & Milerad, 1997; Kong, Hallbeck, & Jung, 2010).
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The cross-talk issue is particularly relevant in cases where the timing of activation of different muscles is of importance, such as in movement analysis (Sasidhar, 2013). The problem becomes even more critical for muscles those are in close proximity with each other (Farina, Merletti, Indino, Nazzaro, & Pozzo, 2002). For instance, the human forearm consists of several muscles in close proximity, and thus there is a relatively small surface area on the forearm for placing the recording devices.
Therefore, analyzing cross-talk from the forearm muscles is an intriguing issue for certain clinical applications such as prosthesis control and pre-and -post operative units for monitoring the activity of muscle of interest, because cross-talk can modify the activity of the muscle, which may misinterpret the intended muscle function. In particularly, most of the clinic relies on the muscle on/off activity that may be altered by the cross-talk (Merlo & Campanini, 2010). Thus, this study focused to understand on cross-talk between the MMG signals recorded from muscles in the forearm.
1.3 Motivation of the Research
To the best of our knowledge, a limited number of studies have investigated the cross-talk in sEMG signals from the forearm muscles (Kong, et al., 2010; Mogk & Keir, 2003; Yung & Wells, 2013). However, it is unclear whether the cross-talk occurs in the forearm muscle as a result of wrist posture and different levels of the gripping tasks.
Since the forearm muscle group plays an important role for the finger and wrist movements and gripping an object in our daily activities, the activities from an individual muscle of the forearm need to be ensured for comprehensive examination of muscle function in rehabilitation centers and controlling prosthesis devices precisely in the clinics. However, the sEMG signal generated by an individual muscle of the forearm
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is very challenging, because sEMG requires two electrodes to place over a muscle whereas the muscle consists of very limited surface area for the recoding electrodes (Kong, et al., 2010; Riek, Carson, & Wright, 2000). Additionally, the volume of conductor in sEMG signal of a muscle may be changed as a movement, which may also recruit geometrical artifact to sEMG signals (Rainoldi et al., 2000). In view of this, MMG may be a better selection for the muscles that have limited surface area and close to each other like the forearm, because the MMG needs only single transducer to place over a muscle. However, before applying the MMG technique over forearm muscles convincingly, the cross-talk between adjacent muscles needs to be revealed out.
Although some studies (Beck, et al., 2010; Cramer et al., 2003) have examined the crosstalk of MMG signals from quadriceps, those studies did not examine the propagation axes of the muscle. This is important because crosstalk is highly associated with the propagation properties of muscle fibre oscillation and becomes even more critical for muscles that in close proximity with each other (Jaskólska et al., 2004).
Therefore, crosstalk in MMG signals from quadriceps muscles may not hold true for the muscles in the forearm, because quadriceps muscle group consists of larger surface area for placing sensor than the forearm. Unfortunately, no previous study to date has examined cross-talk between the MMG signals detected from the forearm muscles. This gap motivates the current study to answer some interrelated research questions.
1.4 Research Questions
i) Does cross-talk occur for MMG signals generated by the different axes of the forearm muscles during all wrist postures?
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ii) Do the wrist postures influence the level of cross-talk in the MMG signals generated by the forearm muscles?
iii) Is there any relationship between the level of cross-talk in the MMG signals generated by the forearm muscles and the submaximal to maximal isometric muscle contractions of the grip force?
iv) Does cross-talk occur for MMG signals due to: force tremor (MMGFT) in the limb, the signal component of intramuscular pressure waves produced by the muscle fibres geometrical changes of fast-twitch fibres (MMGFF) and slow-twitch fibres (MMGSF)?
1.5 Research Objectives and Hypotheses
The overall objective of this thesis was to analyze the cross-talk in the MMG signals generated by the longitudinal (Lo), lateral (La) and transverse (Tr) axes of the extensor digitorum (ED), extensor carpii ulnaris (ECU), and flexor carpii ulnaris (FCU) muscles during the isometric wrist postures of wrist flexion (WF) and extension (WE) and radial (RD) and ulnar (UD) deviations. Additionally, the effect of the wrist postures and different levels of grip force on the cross-talk values was also analyzed. Specifically the objectives are:
i) To quantify and analyze the level of cross-talk in the MMG signals from the Lo, La and Tr axes of the ED, ECU, and FCU muscles during the WF, WE, RD, and UD wrist postures.
Our hypothesis suggests that the multi-axis MMG signals from forearm muscles may show different levels of cross-talk due to the effects of the propagation properties of muscle fibres forming the MMG signal. Therefore, any of these axes may accumulate
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