EMG-Based Human Machine...
Transcript of EMG-Based Human Machine...
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1April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Advised by Drs. Yufeng Lu and In Soo Ahn
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2EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
April 28th, 2018
Introduction
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3April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
EMG Explained
• Electromyography (EMG)
o Measurement of electrical signals
produced by activity in the muscles
• Surface EMG (sEMG):
o A technique for acquiring EMG
signals by using electrodes placed
on the skin, directly above the
desired muscle
Figure 1: Myo armband and forearm
muscles [1]
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4April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
EMG Explained
• Raw EMG signals have a peak-to-
peak voltage of up to 10 mV
• Typical frequency range is 10 Hz
to 500 Hz
• Signal strength and patterns vary
from person to person
Figure 2: Raw EMG data from eight sensors
while holding a fist
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5April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Applications of EMG
• Primarily for medical purposes
o Diagnosing and testing for muscle
and nerve injuries
• Control of prosthetic limbs
• Gesture control
o Drones/robots
o Computers
Figure 3: EMG test [2]
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6April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
• Developed an EMG-based human
machine interface for a mobile robot
• Only 1 EMG sensor was used. Precise
sensor placement was required.
• Neural network was used to identify
predefined motions
• Careful calibration from the user was
needed.
Figure 4: EMG system setup from
previous year’s senior project [3]
Previous Work – EMG HMI (2016-2017)
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7EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
April 28th, 2018
Objectives
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8April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Project Goals
This project aimed to design an EMG-based human
interface system which can:
1. acquire EMG data from the user
2. detect different user hand gestures in real time
3. implement gesture detection to control a system
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9April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Project Goals
1. Acquire EMG data from the user
a) Sufficient sampling rate
b) Wireless communication
c) Reliable connectivity
2. Detect user hand gestures in real time
a) Good user experience: quick calibration, fast and accurate
gesture recognition
b) Provide user with feedback through the console
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10April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Project Goals
3. System specifications
a) Motors
i. 180° rotation
ii. Smooth movement
b) Cameras
i. 30 frames per second
ii. 720p resolutionFigure 5: System hardware
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11EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
April 28th, 2018
Gesture Detection
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12April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Initial Data Collection
• Objectives
o Understand the raw EMG data from Myo
armband
o Confirm that they could be used to
differentiate between hand movements
• Setup
o Record and save raw EMG data with Visual
Studio (C++)
o Plot the saved data in MATLAB
2
1 8 7
6
Figure 6: Myo Gesture Control
Armband with labeled sensors
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13April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Initial Data Acquisition
Figure 7: Raw data from waving wrist outward
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14April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Data Processing
Figure 8: Progression from raw to filtered data
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15April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Sensor Activation Patterns
Figure 9: Sensor histograms for different hand gestures
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16April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Calibration• Prompt user to hold each gesture for roughly
5 seconds
• Take averaged data from each sensor
• Grouped the data in consecutive groups of
three
o [123, 234, … 781, 812]
• Identify the three groups with the highest
activation levels
o Stored in a lookup table
User 1 1 2 3
FIST 8 1 7
IN 7 6 5
OUT 2 3 1
User 2 1 2 3
FIST 1 8 7
IN 6 5 7
OUT 2 1 3
Table 1: Comparison of calibrations
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17April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Data Processing
• A circular buffer is used to save 100
samples (0.5 seconds at 200 Hz) of
data from each sensor
• Compute two averages
o Average of each of the eight sensors
o Average of the whole buffer
Figure 10: Circular Buffer
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18April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Detection Algorithm
• Group the average value of all sets of three consecutive
sensors
o [123, 234, … 781, 812]
• Identify the top three most active groups
• Compare the top three groups to those from calibration
data
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19April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Detection Algorithm
• Assign weights to different matches
between calibration and real time
data
• Maximum possible confidence level:
20
• If confidence ≥ 10 for 0.75 seconds,
gesture is confirmed
Calibration
Real
Tim
e D
ata
1st 2nd 3rd
1st 10 7 3
2nd 4 6 2
3rd 2 3 4
Table 2: Confidence levels for
different pairings of top sensor
groupings
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20April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Detection Algorithm
Wave In Groupings
7 6 5
7 10 7 3
6 4 6 2
2 2 3 4
Wave Out Groupings
2 1 3
7 10 7 3
6 4 6 2
2 2 3 4
FIST GroupingsR
eal
Tim
e
Gro
up
ing
s8 1 7
7 10 7 3
6 4 6 2
2 2 3 4
FIST confidence = 3 Wave In confidence = 16 Wave Out confidence = 2
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21April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Detection Algorithm
Wave In Groupings
7 6 5
7 10 7 3
6 4 6 2
2 2 3 4
Wave Out Groupings
2 1 3
7 10 7 3
6 4 6 2
2 2 3 4
FIST GroupingsR
eal
Tim
e
Gro
up
ing
s8 1 7
7 10 7 3
6 4 6 2
2 2 3 4
FIST confidence = 3 Wave In confidence = 16 Wave Out confidence = 2
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22EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
April 28th, 2018
System Design
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23April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
System Flowchart
Figure 11: System flowchart
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24April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
System Flowchart
Figure 11: System flowchart
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25April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
System Flowchart
Figure 11: System flowchart
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26April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
System Flowchart
Figure 11: System flowchart
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27April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Specifications – Myo Armband
• 8 EMG sensor electrode pairs
• Sampling rate: 200 Hz
• EMG data sent via Bluetooth 4.0 LE
o 8-bit signed integer
o Unitless, represents muscle activation
• Compatible with:
o Windows 7, 8, and 10
o OSx 10.8 and up
o Android 4.3 and up
2
1 8 7
6
Figure 12: Myo Gesture Control
Armband with labeled sensors
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28April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Hardware Diagram
Figure 13: System hardware diagram
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29April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
System Hardware
Figure 14: System hardware
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30April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Specifications – Raspberry Pi 3B• Quad Core 1.2GHz Broadcom BCM2837 64bit CPU
• Bluetooth (BLE 4.1) and Wireless LAN
• Boots from Micro SD
• Runs Raspbian OS
• Input / Output
o 40-Pin extended GPIO
o CSI Camera port for Raspberry Pi Camera
o 4 Port USB
o HDMI out
o Ethernet
Figure 15: Raspberry Pi 3B [4]
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31April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Specifications – Raspberry Pi Camera
• Compatible with Raspberry Pi 3B
• 8 Megapixel Sony sensor
• Capable of video and photo
• 15 cm ribbon cableFigure 16: Raspberry Pi Camera
v2 [5]
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32April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Table 3: Bill of materials
Bill of Materials
Part Quantity Cost
Myo Gesture Control Armband 1 $200
Raspberry Pi 3B 3 $150
Raspberry Pi Camera v2 2 $50
Raspberry Pi Case 2 $36
16 GB micro SD card 3 $30
5-port Ethernet Switch 1 $10
Ethernet Cables (1 ft) 5 $10
Total Cost $ 486
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33April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Armband Communication
• Based on PyoConnect_v2.0 [6]
o Designed to function like the first
party software
o Built for a Linux environment
instead of Windows
• All code written in Python
2.7.14
Figure 17: PyoConnect_v2.0 User
Interface
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34April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Video Stream
• Used RPi-Cam-Web-Interface
o Firmware and API are open-
source [7]
• Each feed streams to a local
webpage
• Fully configurableFigure 18: Screenshot of the
camera’s webpage
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35April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Servo Control
• Controlled with PWM
o Using python module RPi.GPIO
• Servo motors
o Pros:
▪ From the ECE lab inventory
▪ Quickly integrated into the system
o Cons:
▪ Insufficient torque
▪ Jittery motion
▪ Delayed response
Figure 19: Servo motor mounted
atop Raspberry Pi
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36April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Demo Video
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37EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
April 28th, 2018
Project Management
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38April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Tracking of Work – Trello
Figure 20: Project management Trello board
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39April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Division of LaborAditya Patel Jim Ramsay
Communication
• Video feed communications
• System component communication network
Programming
• Raspberry Pi 3B setup
• Pi GPIO configuration
• Development of code architecture
Deliverables
• Website
• Powerpoints
Data Collection and Analysis
• Data analysis
• Gesture detection algorithm
• Exploring neural network
Camera Hardware Design
• Case
• Mounts
• Servo motor wiring
Deliverables
• Figures
• Reports
Table 4: Division of Labor
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40April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Schedule
November
1. Write proposal
2. Submit parts list to Chris Mattus
3. Get raw data from armband
4. Discuss/consider filtering options
5. Make website
6. Draft project proposal presentation
7. Practice presentation
8. Revise proposal for final submission
December
1. Finalize the website design
2. Present project proposal
3. Submit all deliverables
Winter Break
January Winter Break
1. Compare gesture detection options and
choose which to continue with
2. Begin raspberry Pi development
Table 5: Schedule (part 1)
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41April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Schedule
February1. Configure Raspberry Pi and peripherals
2. Hardware design and building1. Code development
March 1. Code development1. Make poster
2. Refine the code and hardware
April1. Finalize all code
2. Present at Student Expo
1. Create presentation slides
2. Practice project presentation
3. Submit project report
Table 6: Schedule (part 2)
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42EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
April 28th, 2018
Conclusions
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43April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Looking Forward
• Incorporate more gestures
o Fingers spread
o Pinch fingers
o Rotate a closed fist
o Add a second armband
• Explore biomedical applications such as
controlling a prosthetic hand
o Use an embedded system with more
computation power for advanced algorithms
o Requires higher precision control
Figure 21: Robotic hand [8]
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44April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Looking Forward
• Neural network
o Developed in MATLAB
o Tested on saved data
o 86% accuracy
o Plenty of room for
improvementTable 7: Confusion matrix from
neural network
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45April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
Recap
This project successfully created an EMG-based human
machine interface that:
1. acquired EMG data from the user
2. can detect and recognize user hand gestures in real time
3. implemented gesture detection to control a system
4. laid a solid foundation for future development
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46EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Conclusions
Introduction
Objectives
Gesture Detection
System
Design
Project Management
April 28th, 2018
Questions?
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47April 28th, 2018EMG-Based Human Machine Interface
Aditya Patel and Jim Ramsay
Sources
[1] http://teachmeanatomy.info/upper-limb/muscles/posterior-forearm/
[2] https://www.healthline.com/health/electromyography
[3] 2016-2017 Bradley University EMG HMI, John Moron, John Cochrane, Thomas DiProva.
<http://ee.bradley.edu/projects/proj2017/emg/>
[4] https://www.raspberrypi.org/products/raspberry-pi-3-model-b/
[5] https://www.raspberrypi.org/blog/new-8-megapixel-camera-board-sale-25/
[6] http://www.fernandocosentino.net/pyoconnect/
[7] https://elinux.org/RPi-Cam-Web-Interface
[8] https://cdnb.artstation.com/p/assets/images/images/005/648/463/large/prashan-s-robotic-hand.jpg?1492700295