This document summarizes a project to design and implement an EMG-controlled 3D printed prosthetic arm. Key aspects include: 1) An analog front-end circuit was designed to record muscle electrical signals (EMGs) from surface electrodes and filter the signals. 2) A prosthetic arm design was 3D printed with 5 fingers and 5 degrees of freedom. Servo motors control finger movement. 3) EMG signals are analyzed in MATLAB and used to detect muscle contractions and control the prosthetic arm motions in real-time using an Arduino microcontroller. 4) Testing showed the system achieved over 84% accuracy in gesture recognition with a response time of 1.5 seconds using a

1. Paper ID 39
Design and Implementation of an EMG Controlled 3D
Printed Prosthetic Arm
Authored by
Nazmus Sakib and Md. Kafiul Islam
Presented by
Md. Kafiul Islam, PhD, SMIEEE
Asst. Prof., Dept. of EEE, IUB
kafiul_islam@iub.edu.bd
1

2. Presentation Outline
 Introduction
Motivation and Objectives
Electromyography (EMG) Basics
State-of-the-Art Prosthetic Systems
 Proposed System Design & Implementation
 Analog Front-End (EMG Recorder) Design & PCB Implementation
 Signal Analysis in MATLAB
 Prosthetic Arm Design & 3D Printing
 Control Circuitry
 Experiment and Testing
 Performance Evaluation & Comparison
 Conclusion & Future work
2

3. Motivation and Objectives
3
Motivation:
• > 4000 people get injured in road accident
every year and live without one or more
body parts.
• Rana plaza massacre and many other left
many people cripple for life time.
• Dependency on imported expensive
prosthesis.
Goal:
Provide an affordable solution of prosthetic limb for our country
Objectives:
• Design an Analog Frontend circuit to record the electrical
activity of muscle (EMG).
• Design and 3D print a prosthetic arm
• Detect muscle contraction to generate a command signal to
control prosthetic arm.
0
2000
4000
2009 2010 2011 2012 2013 2014 2015 2016
(Up to July)
ROAD ACCIDENT STATISTICS
Accidents Desths Injury

4. Electromyography (EMG)
4
• Electromyography (EMG) detects electrical potential
generated by muscle cells upon voluntary contraction
of muscle.
• Brain commands in spinal cord transmitted to muscle
fiber by Motor neuron.
• When motor unit is activated, muscle fibers contract.
Motor Unit Action Potential (MUAP)

5. EMG Signal Characteristics
5
Typical MUAP Waveform
Biopotential Frequency Range Signal Amplitude Electrode
Electromyogram
(EMG)
20 Hz – 1000 Hz
Maximum Usable Energy:
50 Hz - 150 Hz
10 µV – 2mV Surface

6. 6
Currently Available Prosthetic Systems*
Touch Bioinics Open Hand Project Bebionics
Price range: USD 10K
Products:
• i-limb revolution
• i-limb ultra
• i-limb digits
• livingskin
• Price range: USD 1K
• They are using 3D
printer technology
• Price range:
Between USD 11-
60K.
• Most advanced
prosthetic
technology
• Human like hand
movement system.
Price range: USD 6K
Products:
• Brunel Hand 2.0
• Hero Arm
Open Bionics
*Ref: [2]-[5]

7. 7
Proposed System

8. Electrodes & Connector
 Using surface EMG (SEMG), made of Ag/AgCl.
 Permits electron conduction from the skin to the wire.
 The connectors of these electrodes have three conductor
sensor cable with electrode pad leads.
 Using electrolytic gel between skin and electrode can
reduce electrode impedance.
 Multi-useable electrodes, positive, negative and ground,
to the skin/surface covering the muscle, in order to
detect muscle movement.
8
Connectors & Electrodes used in this Project
Electrodes

9. Dual DC Voltage Supply9
• To provide constant voltage and protect
circuit IC components.
• 5V Boost converters are used to provide +5V
and -5V output.
• 3.7V Lipo or Li-ion both type of batteries can
be used.

10. Instrumentation Amplifier
10
• AD620: Low cost, high accuracy with high input impedance, low DC offset,
low noise and very high open-loop gain.
• High CMRR: >100dB
• Amplitude of input: 0.01mV to 1 mV.
• Gain = 1+
49.4𝑘𝞨
10
≈ 5000 (for Full swing of the ADC)
• Takes the difference between two electrodes and amplify it.

11. Active High-Pass Filter
11
• To remove low frequency noise
interference and DC offset.
• Cutoff Frequency:
1
2π R1R2C1C2
= 3 Hz
• Order of filter: 2nd order
• Gain: Unity
• Output: Non inverted

12. Active Low-Pass Filter
12
• Remove high frequency noise interference.
• Cutoff Frequency:
1
2π R1R2C1C2
= 1000 Hz
• Order of filter: 2nd order
• Gain: Unity
• Output: Non inverted

13. 13
Analog Filters Simulation
• LM358 dual Op-Amp IC was used in
this Project.
• Both filters ware designed and
simulated in Porteous using LM358.
• The PCB (printed circuit board) was
also designed in Porteous Software
LM358

14. 14
PCB Design of Analog FE
3D view of PCB with componentsPCB Layout

15. 15
EMG Recorder Circuit (Analog FE)
AD620
LM358
RGain
Pins to Connect
Power Supply
Electrode Connector
Pins
Output

16. 16
Complete EMG recorder Circuit
Complete EMG Recorder FE

17. Recorded EMG Signals
EMG signal Recorded shown on Oscilloscope
20/40
18

18. Signal Analysis in MATLAB
0 50 100 150 200
5000
10000
15000
Frequency (Hz)
Magnitude
Single frequency responce when there is no muscle contraction
0 50 100 150 200
2000
4000
6000
8000
10000
Frequency (Hz)
Magnitude
Single frequency responce when muscle contracts
21/40
19

19. 0 50 100 150 200
2000
4000
6000
8000
10000
Frequency (Hz)
Magnitude
Single frequency responce when there is no muscle contraction
0 50 100 150 200
2000
4000
6000
8000
10000
Frequency (Hz)
Magnitude
Single frequency responce when muscle contracts
Signal Analysis in MATLAB (Cont…)
• Filtering in Digital domain
• Infinite impulse response (IIR) Filter
• Notch Filter
• To remove 50Hz power line noise
• Order of filter: 2nd order
• Stability: Stable
• HPF
• Cutoff frequency: 10Hz
• Order of filter: 5th order
• Stability: Stable
• LPF
• Cutoff frequency: 150Hz
• Order of filter: 4th order
• Stability: Stable
Power line noise & other noise removal
20

20. Trial
No.
SNR with
Background
Noise (dB)
SNR after
Filtering
(dB)
Improvement in
SNR after
Filtering (dB)
1 38.08 65.89 27.81
2 36.52 65.11 28.58
3 39.12 67.74 28.61
4 38.28 66.64 28.35
5 42.05 70.74 28.68
Average 38.81 67.22 28.41
Signal Analysis in MATLAB (Cont…)
Signal Improvement Analysis by calculating SNR
EMG signal before and after filtering
𝐒𝐍𝐑 = 𝟐𝟎𝐥𝐨𝐠 𝟏𝟎
𝑺𝒊𝒈𝒏𝒂𝒍 𝑷𝒐𝒘𝒆𝒓
𝑵𝒐𝒊𝒔𝒆 𝑷𝒐𝒘𝒆𝒓
21

21. Prosthetic Arm 3D Design
• Open Source Design
• Designed and modified in
TinkerCAD online free software
22

22. No. Name of the part
No. of
joints/parts
Length
/Weight
1 Thumb finger 2 5.5 cm
2 Index finger 3 6 cm
3 Middle finger 3 8.5 cm
4 Ring finger 3 7.5 cm
5 Pinky finger 3 5.5 cm
6 Palm 1 10 cm
7 Wrist 4 23 cm
8
Diameter at end of
wrist
̶̶̶̶̶̶̶̶̶ 10 cm
9
Total length of the
Arm
̶̶̶ 41.5 cm
10 In-fill ̶̶̶ 20%
11 Weight ̶̶̶
300gm
(approx.)
3D Printing (Technical Specs)
• Printed in PRUSA mk3
• PLA filament
• Speed 6.09gm/min
23

23. 3D Printed Arm
• 5 Actuators (MG90s) for 5
Fingers
• 5 DOF
• Elastic foe flexibility
• Strong thread as Tension wire
• Extra supports for more stable
finger movement
24

24. Control Circuitry
Circuit includes
o 2 Arduino pro-mini
o EMG recorder Circuit
o Voice recognition module
o Prosthetic Arms actuators (MG90s servo motors)
1st Arduino
• It gets signal from EMG recorder
• Process the signal in digital domain using the coefficients
calculated in MATLAB.
• detects muscle construction by setting up a threshold and
send a pulse to the 2nd Arduino
2nd Arduino
• It detects the pulse from 1st Arduino.
• For the 1st pulse the Arduino sends signal to the arm to perform
a gesture and when it gets another pulse it sends a signal to relax
the arm.
• This Arduino parallelly checks the voice command. Whenever a
different voice command is given the prosthetic arm performs a
different gesture when it gets a pulse from 1st Arduino.
25

25. Gesture
Mode
Thumb Index Middle Ring Pinky
Grab 130° 150° 160° 155° 130°
Pick 130° 150° 0° 0° 0°
Open 0° 0° 0° 0° 0°
Control Circuitry (Cont…)
• Prosthetic Control
o Each finger is connected to a servo motor by a tension wire.
Whenever the servo rotates the fingers of the arm move to
perform a gesture
o Servos are controlled by PWM signal with fixed frequency of 50
Hz.
o All servos receive individual PWM signal from the 2nd Arduino.
By varying the pulse width the angular rotation varies.
o For different gestures the PWM signal changes differently for
each finger as a result the each individual motor has individual
angular rotation
Initial
Angle
Maximum
Angle
26

26. 2 Different gestures performed for different task
Grabbing of Objects by the Arm27

27. Prosthetic Arm
1st Arduino
EMG Recorder
Circuit
Servo motors
Voice
Recognition
Module
2nd Arduino
Complete System
28

28. 28
Performance Evaluation (Accuracy)
0
20
40
60
80
100
Subject 1 Subject 2 Subject 3 Subject 4 Subject 5 Subject 6 Total
Accuracy
1st finger 2nd finger 3rd finger
𝐴𝑐𝑐 =
𝑇𝑃 + 𝑇𝑁
𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁
Average Accuracy per subject is around 84%!
• 6 subjects
• Each subject had 5 trials
• And 6 commands in each trial
• In total 180 trial samples for
accuracy calculation

29. 29 Performance Evaluation
(Response Time & Power Consumption)
Supply
Voltage
Current
Consumption
(No load)
Current
Consumption
(Full load)
Total Power
Consumption (Full
load)
Overall System Response
Time
5V 0.5A 2.6A 13W 1.5 sec (approx.)
• 7.4V 6000mAh Li-ion battery
• Regulated using 7805 voltage regulator

30. (Component) Cost Analysis
30
SI Component Unit Cost (BDT)
1 Electrodes 3 30
2 Connectors 1 20
3 PCB 1 50
4 AD620 1 220
5 LM350 Op-Amp 1 10
6
Electrical components (resistor capacitor,
connector, wires, LED, Vero-board, regulators
etc.) ̶ 200
7 Lipo Batteries 2 400
8 Li-ion Batteries 6 300
9 Boost Converters 2 100
10 Arduino Pro-Mini 2 300
11 Prosthetic Arm (printing cost) 1 1000
12 Servo motor MG90s 5 750
13 Voice recognition module V3 1 2000
14 Others (glue, tension wire, elastic etc.) ̶ 100
Total cost 5480
Commercial EMG recorder
available in Bangladesh
(Price: 5150 BDT)

31. 31
Comparison with Other Works
Ref
No.
Biomedical recorder Robotic/Prosthetic hand Extra feature
1 Commercial EMG recorder In house 3D printed Prosthetic Hand
No Extra Feature
2 In house-built EMG recorder Not enough info available
3 Commercial EMG recorder No prosthesis / simulated controlling
4
Commercial biomedical
signal recorder
In house built Robotic arm
5 In house-built EMG recorder Commercial robotic arm
6 Not enough info No prosthesis / simulated controlling
7 Commercial EMG recorder Commercial 3D printed Prosthetic Hand
8 Commercial EMG recorder No prosthesis / simulated controlling
9 Not enough info In house built Prosthetic Hand
10 Commercial EMG recorder No prosthesis / simulated controlling
11 Commercial EMG recorder
In house-built hand rehabilitation robotic
system
12 Commercial EMG recorder In house 3D printed Prosthetic Hand
13
Not used
Simulation test
Voice Recognition14 In house 3D printed Prosthetic Hand
15 Commercial robotic arm
This
One
In House built EMG
recorder
In house 3D printed prosthetic arm Voice Recognition

32. Conclusion
 The EMG signal and Voice command controlled Prosthetic system
developed in this project is less expensive and can be affordable for
people in developing country like Bangladesh.
 The system was successfully designed and tested on an amputee
individual.
 Since the whole system is designed and developed here, so if any
modification required, can be possible.
32

33. Future Works
 Making the prosthetic arm more user friendly
 Using Dry electrodes
 Making an in-house built voice recognition module with local recources
 Improving the design of the prosthetic arm
 Increasing the degree of freedom of the prosthetic arm
 Making the project available for the people so that they can have the benefits of
this project by improving their quality of life.
 EMG signal based diagnosis of neuromuscular diseases.
33

34. 34
Sample Video of Experiment

35. References
35
1. Yazicioglu, Refet Firat, Chris Van Hoof, and Robert Puers. Biopotential readout circuits for portable acquisition systems. Springer Science & Business Media, 2008.
2. https://www.ottobockus.com/prosthetics/upper-limb-prosthetics/solution-overview/bebionic-hand/ (assessed on 02/09/2019)
3. https://www.touchbionics.com/(assessed on 02/09/2019)
4. http://www.openhandproject.org/(assessed on 02/09/2019)
5. https://openbionics.com/(assessed on 02/09/2019)
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36. Thank You!
36