(Đồ án hcmute) design and fabricate emg anlysis system

BỘ GIÁO DỤC VÀ ĐÀO TẠO
TRƯỜNG ĐẠI HỌC SƯ PHẠM KỸ THUẬT
THÀNH PHỐ HỒ CHÍ MINH

ĐỒ ÁN TỐT NGHIỆP
DEPARTMENT OF MECHATRONICS TECHNOLOGY

DESIGN AND FABRICATE EMG ANALYSIS SYSTEM

GVHD: Dr. BUI HA DUC
SVTH : TRAN HUU TRONG
MSSV: 14146292
SVTH : NGUYEN MANH CUONG
MSSV: 14146026
SVTH : NGUYEN HONG PHUC
MSSV: 14146271

SKL 0 0 5 2 4 7

Tp. Hồ Chí Minh, tháng 07/2018

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HCMC UNIVERSITY OF TECHNOLOGY AND EDUCATION
FACULTY OF QUALITY TRAINING
DEPARTMENT OF MECHATRONICS TECHNOLOGY

GRADUATION THESIS
DESIGN AND FABRICATE EMG ANALYSIS SYSTEM
INSTRUCTOR:

Dr. BUI HA DUC

STUDENT’S NAME:

TRAN HUU TRONG

14146292

NGUYEN MANH CUONG 14146026
NGUYEN HONG PHUC
MAJOR:

14146271

MECHATRONICS

Ho Chi Minh City, July 2018

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ABSTRACT
EMG is a bioelectrical signal produced by muscle activity. EMG has
a lot of advantages for applying in control field. Nowadays, there are
many research using the EMG signal to control the robot arm and
peripheral devices. However, acquisition of EMG signal have some
challenges… In this research, our group focus on designing and

fabricating the EMG acquisition system in real time.
EMG acquisition system circuit including ADS1293 communicate
with Raspberry 3 via SPI protocol. This circuit using electrodes for
collecting signal from surface skin. The collected signal is processed,
analyzed on acquisition system circuit and displayed on the computer.
During the research, our group successfully fabricate the EMG
acquisition system with low cost and compact size. The signal is collected
accurately which represent the full characteristic of muscle activity, the
results of this research show the EMG acquisition system could be
response particularly the acquisition, processing and analysis.
This project opens opportunity to the development and application of
EMG signals in the technical control field. It can become one of the most
widely used scientific fields in the future.

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ACKNOWLEDGEMENT
We sincerely thank the lecturers of the department of mechatronics of
high quality training who have continuously encouraged us and also provided
precious knowledge. Without it, we could not complete our project.
We would like to express my special thanks to Dr.Bui Ha Duc who
supported us a lot for doing this study and we explored so many new things.
The group is looking forward to receiving feedback, instructions of
lecturers to revise and improve the project better.
My group sincerely thanks!
Ho Chi Minh City, July 2018
Group of students: Tran Huu Trong

Nguyen Manh Cuong
Nguyen Hong Phuc

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Contents
List of figures.................................................................................................... 6
List of Tables ..................................................................................................... 9
List of Abbreviations ....................................................................................... 10
Chapter I: INTRODUCTION ......................................................................... 11
1.1. Motivation .......................................................................................... 11
1.2. Project objectives................................................................................ 11
1.3. Scope of research ................................................................................ 12
1.4. Thesis organization ............................................................................. 12
Chapter II: LITARETURE REVIEW ............................................................. 13
1.5. Theory of EMG .................................................................................. 13
1.5.1. Definition ..................................................................................... 13
1.5.2. Medical uses ................................................................................. 14
1.5.3. Typical benefits ............................................................................ 15
1.5.4. EMG signal .................................................................................. 15
1.5.5. EMG Amplification...................................................................... 19
1.6. Surface EMG electrodes placement ................................................... 21
1.6.1. Surface electrode .......................................................................... 21
1.6.2. Muscle map frontal....................................................................... 22
1.6.3. Forearm electrode placement ....................................................... 22
1.7. EMG signal analysis ........................................................................... 28
1.7.1. Amplitude and frequency of EMG Signal ................................... 28

1.7.2. Pre-processing .............................................................................. 29
1.7.3. Data analysis ................................................................................ 29
1.8. Theory of Neural Networks ................................................................ 30
1.9. Overview of previous research ........................................................... 31
Chapter III: DESIGN AND FABRICATE EMG ACQUISITION SYSTEM 35
3.1. Method 1: Using OPAMP .................................................................. 35
3.1.1. Amplifiers and filters ................................................................... 35
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3.1.2. Amplification and filtering circuitry ............................................ 40
3.1.3. Final design .................................................................................. 45
3.1.4. Simulation results ......................................................................... 49
3.1.5. Collect EMG signal experiment in practical ................................ 54
3.2. Method 2: Using ADS1293 and Raspberry Pi 3 ................................ 55
3.2.1. Data acquisition with ADS1293................................................... 55
The EMG signal is collected using surface EMG electrodes. These signals
are processed by a circuit including ADS1293 and Raspberry Pi 3, data
from processing via SPI protocol is transferred to computer via wireless
network for program and control. ............................................................. 55
3.2.2. Design acquisition system circuit ................................................ 64
3.2.3. The EMG acquisition system circuit ............................................ 69
Chapter IV: DESIGN PROTOCOL EXPIREMENT .................................... 70
4.1. Interface between ADS1293 with Raspberry Pi 3 ............................. 70
4.1.1. SPI ................................................................................................ 70
4.1.2. SPI communication between the ADS1293 with RPi3................ 73
4.2. Raspberry Pi 3 interface to WEB ....................................................... 78
4.2.1. WEB – Server............................................................................... 78

4.2.2. Using Node.js to transmit data to Pusher ..................................... 80
4.2.3. Using canvas.js min to draw plot of dataPoint on browser ......... 82
Chapter VI: RESULT AND ANALYSIS ....................................................... 83
6.1. Ambient noise ..................................................................................... 83
6.2. Sinwave/squarewave ............................................................................. 83
6.3. ECG ....................................................................................................... 89
6.4. EMG ...................................................................................................... 90
Chapter VII: CONCLUSION.......................................................................... 92
REFERENCES ................................................................................................ 93
ADDENDUM.................................................................................................. 94

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List of figures
Figure 1. EMG Signal ..................................................................................... 13
Figure 2.Surface EMG .................................................................................... 14
Figure 3. Medical benefits of EMG ................................................................ 15
Figure 4. EMG Signal ..................................................................................... 16
Figure 5. Motor unit ........................................................................................ 17
Figure 6. Schematic illustration of depolarization/repolarization .................. 18
Figure 7.The action potential. ......................................................................... 18
Figure 8. The "Raw" EMG Signal .................................................................. 19
Figure 9. Electrode leads with cable built-in preamplifiers ............................ 20
Figure 10. The effect of A/D sampling frequency .......................................... 21
Figure 11. Diagram of detecting EMG signal into MUAPTs ......................... 21
Figure 12. Muscle Map Frontal....................................................................... 22
Figure 13. Superficial Muscles ....................................................................... 23

Figure 14. Flexor muscle of anterior forearm ................................................. 26
Figure 15. Extensor muscle of posterior forearm ........................................... 26
Figure 16. Electrode orientation for EMG recording...................................... 27
Figure 17. Electrode Placement for wrist movement...................................... 28
Figure 18. Block Diagram of EMG analysis system ...................................... 30
Figure 19. a) Biological neural networks.
b) Brain ................................. 31
Figure 20. Layers of Neural networks ............................................................ 31
Figure 21. Myo gesture control armband ........................................................ 32
Figure 22. e-Health Sensor Platform............................................................... 33
Figure 23. EMG Sensor................................................................................... 34
Figure 24. Schematic and block diagram of an ideal amplifier with gain of G
......................................................................................................................... 35
Figure 25. An amplifier with different input configuration. ........................... 36
Figure 26. A differential amplifier configured as an inverting and noninverting amplifier ........................................................................................... 36
Figure 27. INA114 Circuit .............................................................................. 37
Figure 28. Idealized Filter Responses ............................................................. 38
Figure 29. Key Filter Parameters .................................................................... 39
Figure 30. Block diagram of amplification ..................................................... 40
Figure 31. INA114 .......................................................................................... 41
Figure 32. Structure of INA114 ...................................................................... 42

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Figure 33. Structure of OP07CP ..................................................................... 42
Figure 34. The 1st Amplifier circuit ............................................................... 43
Figure 35. Low-pass filter circuit .................................................................... 44

Figure 36. High-pass filter circuit ................................................................... 45
Figure 37: The Second Amplifier circuit ........................................................ 45
Figure 38. The 2nd Amplifier circuit .............................................................. 45
Figure 39. Table of components on Altium Designer..................................... 47
Figure 40. Printed circuit board ...................................................................... 47
Figure 41. Image 3D of PCB........................................................................... 48
Figure 42. Top face of the EMG acquisition system circuit. .......................... 49
Figure 43. Simulate the low-pass filter circuit ................................................ 49
Figure 44. Low-pass filter signal at 400Hz ..................................................... 50
Figure 45. Low-pass filter signal at 1000Hz ................................................... 50
Figure 46. Simulate the high-pass filter circuit ............................................... 51
Figure 47. High-pass filter signal at 400Hz .................................................... 51
Figure 48. High-pass filter signal at 4Hz ........................................................ 52
Figure 49. Simulate the 2nd amplification circuit .......................................... 52
Figure 50. Signal at 400Hz.............................................................................. 53
Figure 51. Signal at 1000Hz............................................................................ 53
Figure 52. Block diagram of EMG Acquisition System ................................. 55
Figure 53. EMG Acquisition system............................................................... 55
Figure 54. Medical cable 3 snap electrode lead wire and electrode of 3M
RedDot. ........................................................................................................... 56
Figure 55. Electrode placement on the forearm .............................................. 56
Figure 56. Image of ADS1293 ........................................................................ 58
Figure 57. ADS1293 Connection .................................................................... 59
Figure 58. Function Block Diagram ............................................................... 59
Figure 59. The Flexible Routing Switch for single channel ........................... 60
Figure 60. Top View 28-Pin of ADS1293 ...................................................... 62
Figure 61. PIN Functions ................................................................................ 62
Figure 62. Raspberry Pi................................................................................... 63
Figure 63. Functional Block of Raspberry Pi ................................................. 64
Figure 64. Jack 3.5mm female ........................................................................ 64

Figure 65. Crystal ............................................................................................ 64
Figure 66. ADS 1293 communicate with RPI3 via SPI protocol ................... 65
Figure 67. PCB layout ..................................................................................... 67
Figure 68. 3D PCB .......................................................................................... 68
Figure 69. Top side ......................................................................................... 69
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Figure 70. The acquisition system using ADS1293 and RPi3. ....................... 69
Figure 71. SPI four-wire serial bus ................................................................. 70
Figure 72. Synchronize clock line and data line ............................................. 71
Figure 73. SPI bus: Single master and single slave ........................................ 71
Figure 74. An 8 bit shift register is contain for each SPI device. ................... 72
Figure 75. SCK is defined by CPOL............................................................... 72
Figure 76. Data on the MOSI and MISO is defined by CPHA ...................... 73
Figure 77. SPI Protocol on ADS1293 ............................................................. 74
Figure 78. Define of Transfer function. .......................................................... 75
Figure 79. Functions Read Register and Write Register. ............................... 76
Figure 80. Function to stream data from ADS1293 to Rapberry Pi 3 ............ 77
Figure 81. Syntax to open file result.csv to write data out. ............................ 77
Figure 82. Syntax to write adc_data received into file opened below. ........... 77
Figure 83. Function of Notch filter 50Hz in file .m of MATLAB ................. 78
Figure 84. The Webserver ............................................................................... 78
Figure 85. HTTP Protocol ............................................................................... 79
Figure 86. HTTP Messages ............................................................................. 80
Figure 87. Pusher............................................................................................. 81
Figure 88. Node.js ........................................................................................... 81
Figure 89. Image of baseline in Oscilloscope ................................................. 83

Figure 90. Image of baseline with ODR = 833Hz was plotted in MATLAB . 83
Figure 91. Image of Sinwave in Oscilloscope ................................................ 84
Figure 92. Image of Sinwave was plotted in MATLAB with ODR = 833Hz 84
Figure 93. Image of Sinwave was plotted in MATLAB with ODR = 1580Hz
......................................................................................................................... 85
Figure 94. Image of Sinwave in Oscilloscope ................................................ 85
Figure 95. Image of Sinwave was plotted in MATLAB with ODR = 1580Hz
......................................................................................................................... 86
Figure 96. Image of Sinwave was plotted in MATLAB with ODR = 833Hz86
Figure 97. Image of Squarewave in Oscilloscope .......................................... 87
Figure 98. . Image of Squarewave was plotted in MATLAB with 833Hz ..... 87
Figure 99. . Image of Squarewave was plotted in MATLAB with 1580Hz ... 88
Figure 100. Image of Squarewave in Oscilloscope ........................................ 88
Figure 101.Image of Squarewave was plotted in MATLAB with 1580Hz .... 88
Figure 102. Image of Squarewave was plotted in MATLAB with 833Hz ..... 88
Figure 103. Normal electrocardiograms ......................................................... 89
Figure 104. Image of ECG was plotted in MATLAB .................................... 90

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Figure 105. Image of EMG signal was plotted in MATLAB at 1st and 2nd sec.
......................................................................................................................... 90
Figure 106. Image of EMG signal was plotted in MATLAB at 7th sec. ......... 91

List of Tables
TABLE 1. OF REVELANT MUSCLE, ACTION AND NERVE SUPPLY . 24
TABLE 2. SPI FOUR DIFFERENT MODES ................................................ 73

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List of Abbreviations
EMG - Electromyogram
SEMG – Surface Electromyogram
MUAP - Motor Unit Action Potential
MUAPT - Motor Unit Action Potential Trains
CMRR - Common Mode Rejection Ratio
ADC - Analog to Digital Converter
ECG - Electrocardiogram
EMI - Electromagnetic Interference
INA - Instrumentation Amplifier
SDM - Sigma-Delta Modulator
PCB - Printed circuit board
RPi3 - Raspberry Pi 3
SPI - Serial peripheral interface
SCLK, SCK - Serial Clock
MOSI - Master Output Slave Input
MISO - Master Input Slave Output
SS - Slave Select
CS - Chip Select
CSB - Chip Select Bar
DRDYB - Data Ready Bar
CPOL - Clock polarity
CPHA - Clock phase
HTTP - Hypertext Transfer Protocol

FTP - File Transfer Protocol
SMTP - Simple Mail Transfer Protocol
POP - Post Office Protocol
URL - Uniform Resource Locator
JS - Java Script
CSV - Comma Separated Values
DPS - Data Points

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Chapter I: INTRODUCTION
1.1. Motivation
Electromyogram (EMG) is bioelectrical signal produced by motor
control neuron that transmit signal for active muscle cells, this electric
affected by level of muscle activities. This signal appears before moving of
muscle in about 150ms. That is a big advantage. If the EMG signal is utilized
effectively, it could significantly enhance the robustness of control system.
There were several groups have researched and applied EMG signal into
control some devices. For example, Myo Gesture Control Armband is
researched and manufactured by Thalmic Labs. The EMG signal can be
applied to some devices such as a computer game, digital pointer controller
for zoom in on your slides and touch-free.
However, acquisition of EMG signal have some challenges. First, cost
of parts and devices is very expensive. Second, it is not easily to find in
Vietnam. Usually, we must order this from abroad. Third, some devices are
big or oversized. So, it is unsuitable for portable system. Four, the collected
signal is easily noised by external factors. This require the acquisition system

have to perfect and work correctly. And finally, some parts is unsuitable for
technical control.
To overcome these challenge, our team aims to design a compact,
reliable device for acquiring and analyzing EMG signal. This devices will be
affordable, compactly, easy to use and has capability to work in the real-time.
In this project, our team will use embedded circuit Raspberry Pi 3 to
control an Analog Front End for Biopotential Measurements ADS1293.
Interface SPI is used for transmit and receive data between Raspberry and
ADS1293. Additions, we use wireless connection to push the collected data
onto Cloud and draw plot real-time on Web.

1.2. Project objectives
-

Determine technical requirements of EMG signal acquisition system.
Design and fabricate EMG signal acquisition system circuit.
Collect and analyze the EMG signal.
Transmit and store the signal from Raspberry to the computer via
wireless.
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- Apply the signal to control software on the computer.

1.3. Scope of research
The scope of this project is focus on designing and fabricating the
hardware. Then, we apply the EMG signal which is collected to process and
analyze in EMG acquisition system. We use the filters which integrated in

system to eliminate the unwanted noise. Next, we process data and display on
Web by plot. We have already demoed on the presenter application.

1.4. Thesis organization
This thesis will have 6 chapters.
In chapter 1, we will present the EMG, the generation of EMG signal,
processing the signal and factors affecting the EMG signal.
In chapter 2, we will learn about the EMG, the generation of EMG and
EMG signal. EMG has many benefits and applications and we can apply it for
technical control, …
In chapter 3, we use the ADS1293 and the Raspberry Pi 3 to fabricate
the EMG acquisition system circuit. This circuit collected signal by ADS1293
and RPI3 via SPI protocol and send it to computer via wireless for program.
In chapter 4, we will do some experiment for collecting the EMG signal
from the forearm. This chapter provide us the characteristic of the EMG
signal
In chapter 5, will have the experiment results which have done in
chapter 4. The results on the circuit show quality of collected EMG signal.
We will finger out how to collect EMG signal without ambient noise
In chapter 6, will show research results and research directions.

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Chapter II: LITARETURE REVIEW
1.5. Theory of EMG
1.5.1. Definition
Electromyography (EMG) [1] is an experimental technique concerned

with the development, recording and analysis of myoelectric signals.
Myoelectric signals are formed by physiological variations in the state of
muscle fiber membranes.

Figure 1. EMG Signal
EMG measurement is a medical technique which evaluate and record
the electrical activity produced by skeletal muscles. EMG is performed using
an instrument called an electromyograph to produce a record called
an electromyogram.
An
electromyograph
detects
the
electric
potential generated by muscle cells when these cells are electrically or
neurologically activated. The signals can be analyzed to detect medical
abnormalities, activation level, or recruitment order, or to analyze
the biomechanics of human or animal movement.
Surface EMG (SEMG): A technique in which electrodes are placed on
(not into) the skin overlying a muscle to detect the electrical activity of the
muscle.

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Figure 2.Surface EMG
Surface EMG (SEMG) has some attractive features. Most notably, it
does not involve piercing the skin and does not hurt. However, the American

Association of Electro diagnostic Medicine notes that: "There is in fact almost
no literature to support the use of SEMG in the clinical diagnosis and
management of nerve or muscle disease". Still, the SEMG may prove of value
in the future in helping to monitor the progression of disorders of nerves and
muscles.
1.5.2. Medical uses
EMG testing has a variety of clinical and biomedical applications. EMG
is used as a diagnostics tool for identifying neuromuscular diseases, or as a
research tool for studying kinesiology, and disorders of motor control. EMG
signals are sometimes used to guide botulinum toxin or phenol injections into
muscles. EMG signals are also used as a control signal for prosthetic devices
such as prosthetic hands, arms, and lower limbs.

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Figure 3. Medical benefits of EMG
EMG then an acceleromyograph may be used for neuromuscular
monitoring in general anesthesia with neuromuscular-blocking drugs, in order
to avoid Postoperative Residua Curarization (PORC).
1.5.3. Typical benefits
The use of EMG starts with the basic question: “What are the muscles
doing?”
Typical benefits are:
- EMG allows to directly “look” into the muscle
- It allows measurement of muscular performance
- Helps in decision making both before/after surgery
- Documents treatment and training regimes

- Helps patients to “find” and train their muscles
- Allows analysis to improve sports activities
- Detects muscle response in ergonomic studies
1.5.4. EMG signal
1.5.4.1. Definition
EMG signal is the electrical expression caused by neuromuscular
activation during muscular contraction, depicting the current detected at the

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specific location that is produced by the ionic flow across muscle fiber
membranes and transmitted through intervening tissues.

Figure 4. EMG Signal
1.5.4.2. The motor unit
The motor unit is the most elementary functional unit of a muscle,
generating a motor unit action potential (MUAP) when activated. Repeated
continuous activation of motor units generates motor unit action potential
trains (MUAPT) that are superimposed to form the EMG signal.

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Figure 5. Motor unit
1.5.4.3. The generation of the EMG signal

The excitability of muscle fibers through neural control represents a
major factor in muscle physiology. This phenomenon can be explained by a
model of a semi-permeable membrane describing the electrical properties of
the sarcolemna. An ionic difference between the inner and outer spaces of a
muscle cell forms a resting potential at the muscle fiber membrane
(approximately -80 to -90 mV when not contracted). This causes a membrane
Depolarization which is immediately restored by backward exchange of ions
within the active ion pump mechanism, the Repolarization [1]:

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Figure 6. Schematic illustration of depolarization/repolarization
If a certain threshold level is exceeded within the Na+ influx, the
depolarization of the membrane causes an Action potential to quickly change
from – 80 mV up to + 30 mV. It is a monopolar electrical burst that is
immediately restored by the repolarization phase and followed by an After
Hyperpolarization period of the membrane.

Figure 7.The action potential.
1.5.4.4. The "raw" EMG signal
An unfiltered (exception: amplifier bandpass) and unprocessed signal
detecting the superposed MUAPs is called a raw EMG Signal [1]. In the
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example given below, a raw surface EMG recording (sEMG) was done for
three static contractions of the biceps brachii muscle:

Figure 8. The "Raw" EMG Signal
Raw sEMG can range between +/- 5000 microvolts (athletes!) and
typically the frequency contents ranges between 6 and 500 Hz, showing most
frequency power between ~ 20 and 150 Hz.
1.5.5. EMG Amplification
1.5.5.1. EMG – Amplifiers
EMG-amplifiers [1] act as differential amplifiers and their main
purpose is the ability to reject or eliminate artifacts. The differential
amplification detects the potential differences between the electrodes and
cancels external interferences out. Typically external noise signals reach both
electrodes with no phase shift. The "Common Mode Rejection Ratio"
(CMRR) represents the relationship between differential and common mode
gain and is therefore a criteria for the quality of the chosen amplification
technique. The CMRR should be as high as possible because the elimination
of interfering signals plays a major role in quality. A value >95dB is regarded
as acceptable (11, SENIAM, ISEK).
State of the art concepts prefer the use of EMG pre-amplifiers. These
miniaturized amplifiers are typically built in the cables or positioned on top of
the electrodes (Active electrodes). The main idea of using small EMG preamplifiers located near the detection site is early pick up of the signal,
amplification, (e.g. 500 gain) and transmission on a low Ohm level that is less
sensitive to (cable) movement artifacts.

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Figure 9. Electrode leads with cable built-in preamplifiers
An EMG signal that has not been amplified has typical charges between a few
microvolt and 2-3 millivolt when reading on the skin. The signal is generally
amplified by a factor of at least 500 (e.g. when using preamplifiers) to 1000
(passive cable units). The frequency range of an EMG amplifier (bandpass
settings) should start from 10 Hz high-pass and go up to 500 Hz low-pass.
1.5.5.2. A/D Sampling Rate
Sampling a signal at a frequency which is too low results in aliasing
effects. For EMG almost all of the signal power is located between 10 and
250 Hz and scientific recommendations (SENIAM, ISEK) require an
amplifier band setting of 10 to 500 Hz. This would result in a sampling
frequency of at least 1000 Hz (double band of EMG) or even 1500 Hz to
avoid signal loss.

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Figure 10. The effect of A/D sampling frequency

1.6.

Surface EMG electrodes placement

1.6.1. Surface electrode
When muscle contraction, electrical signal active near the skin's
surface, it is possible to place sensors onto to the skin's surface to detect the
electrical signal, called electrodes. The electrical signal recorded by a surface
electrode is called a surface EMG signal (sEMG) [2]. The sEMG signal is the

summation of all MUAPTs from all active motor units detected by an
electrode.

Figure 11. Diagram of detecting EMG signal into MUAPTs
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1.6.2. Muscle map frontal
Most of the important limb and trunk muscles can be measured by
surface electrodes. The muscle maps show a selection of muscles that
typically have been investigated in the scientific study of human or nonhuman body movement. The two yellow dots of the surface muscles indicate
the orientation of the electrode pair in ratio to the muscle fiber direction. Two
yellow dots are the place to attach the electrode for detect EMG signal [1].

Figure 12. Muscle Map Frontal
1.6.3. Forearm electrode placement

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The forearm contains many muscles, including the flexors and
extensors of the digits. The muscles of the forearm can be divided multiple
groups including those responsible for moving the wrist, four fingers, and
thumb. Flexors are innervated by the median nerve while the extensors are
innervated by the radial nerve [2].

Figure 13. Superficial Muscles
The forearm muscle are divided into muscle responsible for wrist
movement and muscle responsible for hand movement. They are divided by
fascia into the anterior flexors (largely innervated by the median nerve) and
posterior extensors (supplied by the radial nerve), each with superficial and
deep muscle layers. These muscles move fingers via their long tendons

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TABLE 1. OF REVELANT MUSCLE, ACTION AND NERVE SUPPLY

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