Real Time Control of Prosthesis Using EMG

Real Time Control
of Prosthesis
Using EMG
Aviv Peleg
Or Dicker
Supervised by:
Tal Shnitzer
Dr. Oscar Lichtenstein
Introduction
-In the USA alone there are 700,000 below the
elbow amputees
-Current hand prostheses capabilities range
from single action to expensive (>20,000$)
multi-action prostheses
Single function prostheses
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multifunction prostheses
Introduction
•Prosthesis for children is especially
complex problem, since the growth of the
child means many the need for many new
prosthesis as the child grows.
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Current Solution
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Current Solution
•3D blueprints for affordable single action
prosthesis are currently available as “open
source”.
•With today’s technology, affordable
multi-action prostheses can be
developed for all in need.
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Our Solution
Sensor:
Prosthesis
MYO armband
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3D printed hand with the Intel Edison
Input-Output
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The EMG
The muscle
Anatomical structure of muscle
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The motor unit
Generation of the EMG
The EMG signal
*
=
*
=
*
=
Amplitude ranging from
-5mV to 5mV
The frequency spectrum of
the sEMG signal collected
with commonly used
sensors ranges from 0 to
400 Hz
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N−1
X t =
h i ∗ 𝛿(𝑡 − 𝑡𝑘 ) + n(t)
i=0
Project Objectives
Development of a control mechanism for a 3d printed Prosthesis:
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Requirement
Goal
Real Time
Less then 250ms from
command to execution
Reliability
classification success
rate of more than 90%
Portability
Fully portable
Variety of
gestures
Range of 6 different
gestures
Short setup
time
Less then 1 minute for
calibration
All the above and also Affordable
The Prosthesis
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The Prosthesis
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Algorithm Workflow
Calibration
Recording and
labeling
Feature
extraction
pattern
recognition
Hand Control
Recording
Decision
Convert
classification to
servo movements
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Real time
classification
Pre action
Threshold
Movement
Calibration
Calibration
Recording and
labeling
Feature
extraction
pattern
recognition
Hand Control
Recording
Decision
Convert
classification to
servo movements
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Real time
classification
Threshold
Movement
Recording and Labeling
Sensor:
MYO armband
EMG data from sensor:
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Raw data
Feature Extraction
• Mean Absolute Value
• Root Mean Square
• Variance
• Willison Amplitude
• Modified Mean Frequency
• Wavelet Coefficients
• Double Threshold (for rest)
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Feature extraction
Mean Absolute Value
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Data after Feature extraction
Pattern Recognition
• K Nearest Neighbors Algorithm
Different K
• Support Vector Machine
Kernel
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Example of K Nearest Neighbors boundary of decision
Pattern Recognition
• K Nearest Neighbors algorithm
K=3
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Example of K Nearest Neighbors boundary of decision
Hand Control
Calibration
Recording and
labeling
Feature
extraction
pattern
recognition
Hand Control
Recording
Decision
Convert
classification to
servo movements
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Real time
classification
Threshold
Movement
Real Time Recording and
Classification
The hardware :
The software :
Microcontroller Intel Edison + self built shield
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classification code in python
Conversion of Classification
to Servo Movements
Servomotor:
TowerPro MG996R Servo
Command of Servo Angle
engine
Servo 1
Servo 2
Mechanical Output
position
Servo 3
gesture
classification
Thumb
Index finger
Digits
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90°
-90°
90°
Index finger
Closed
Open
Closed
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90°
90°
90°
fist
Closed
Closed
Closed
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-90°
90°
90°
Thumb up
Open
Closed
Closed
Conversion table of classification to servo command to mechanical output
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Movement of Prosthesis
The six different hand
gestures: fist, pinch, point,
rest, Frisbee catch, like.
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Overview
BT
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PWM
Results
Set goal
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Real Time
<250 msec
Reliable
>90%
Portability
Fully portable
Variety
6 gestures
set up time
<1 minute
Demo
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Conclusion
-All the objectives were met, proving the feasibility of our
solution.
-Further work is needed to reach a complete product.
Mostly in regard to subjects outside the scope of this
project.
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Future Developments
Remove need for daily calibration using Domain
adaption algorithm.
Implementation of control and feedback over
amount of force generated.
Combining the sensory control systems.
 Reduction of time from command to execution to
Less then 100ms
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ACKNOWLEDGEMENT
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▪ Dr. Oscar Lichtenshtein
▪ Mr. Sharon Ishar
▪ Prof. Yoav Medan
▪ Mr. Gal Pressman
▪ Mr. Hovav Gazit
▪ Mr. Johanan Erez
▪ Mr. Yair Herbst
▪ Mr. Koby Kohai
▪ Mr. Shlomi Dach
▪ Mr. Vasily Vitchevsky
▪ Mr. Oren Forkosh
▪ Mr. Yossi Bar Erez
Questions?
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