Transcript Final Slides

Group N3: Digital
Control
Wayne Blake
Mary Nsunwara
Reshun Gethers
Project Purpose
Design a system that will efficiently
measure signals produced by muscle
movement and use these signals to
operate a remote control car.
Current Applications
EMG applications have two key categories
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Biofeedback
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Mainly for clinical purposes
Utilized as muscle relaxation and treatment
Also to prevent painful muscle conditions
Communication
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Mostly experimental
Muscle movements that control electronic and computer
devices
Current Products
Prices range from $175 - $2000
Inexpensive, But…
Have no computer interface
 Generally only Useful for muscle training
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Bodily Electrical Activity
Contraction of all muscles is triggered
by electrical impulses called Motor Unit
Action Potentials (MUAP’s)
MUAP’s can be created internally or
externally by devices such as the
pacemaker
Electromyography I
Recording of Motor Unit Action
Potentials (MUAPs) produced by
muscle movement
Frequency Range
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50 – 500 Hz
Voltage Range
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0.75 – 2000 uV
Electromyography II
Underlying Processes
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Differential Amplification
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Signal-to-Noise Ratio (SNR)
Common Mode Rejection Ratio (CMRR)
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CMRR is the Ratio of the Differential Gain to the Common
Mode Gain
Both a High SNR and CMRR are Desired, in Order to
Suppress any Common Noise Signals
Filtering – Removes High Frequency Components
Sampling – More Advanced Digital Signal
Processing
Design Goals
Front End
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Build and test EMG Circuit from Last
Semester
Acquire a transferable A/D converter
Back End
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Determine best way to signal the Remote
Control device
Interfacing to PC
Digitize Signals Measured with EMG
Analog to Digital Converter
 Comparator
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Assign Codes for System Control
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Determine Number and Degree of Muscle
Movements to Execute Commands
Apply Commands on Test System
Initial Design Issues
EMG Circuit
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Will previous design work
Signal Source & Usage
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Which muscles to monitor
A/D Converter & Software
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Need PCI connection on A/D Card
EMG Circuit Diagram
EMG Circuit
Design based on Older Version of the
BrainMaster EEG monitor
Circuit Consists of Two Amplification Stages
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An integrator circuit is utilized in this first stage as
a low-pass filter and its main purpose is to provide
good linearity
Stage two has a gain of 390 for a total gain of
19500 for the entire amplifier. Also provides a
frequency response from 1.7 up to 34 Hz
Circuit Specifications
Gain: 20,000
Bandwidth: 1.7 - 34 Hz
Input Impedance: 10 M
CMRR: 100dB
Signal Source Options
Eyebrows
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Good contraction speed
Difficult to move one eyebrow
Low fat tissue coverage
Jaw muscles
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Better contraction speed
Easier to bite on one side of mouth
Usually covered by more fat tissue thus making it
harder to get a usable signal
A/D Converter
Keithley KPCI-3107
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Maximum sampling rate: 100 kb/s
Input ranges: 0 - 5V, 1V, 100mV, 20mV
12 gain ranges (1, 2, 4, 8, 10, 20, 40, 80, 100,
200, 400, 800)
32 digital I/O lines
Uses DriverLINX Package Software which is able
to use up to 24 software programmable ranges
Equipped with PCI connection
Amplifier Design &
Testing
Duplicated Circuit designed last semester
Tested New and Old Board
Compared and Contrasted signals acquired
from both boards
Found solutions to old and new problems
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Old Board Had Loose or Non-existent Solder Joints
Details Provided on New Board Issues
Carbon Copy
Old Amplifier
New Amplifier
Building Procedure
Duplicated Amplifier From Last Semester
Accuracy and Cost of Our Circuit
Components were Reduced
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Okay because Board Measures Muscle
Movements, which are Larger than Brain Waves
5% Resistors were used in place of 1% Resistors
50V Capacitors were used in place of 400 V
Capacitors
Had To Utilize Adapters to Connect SurfaceMount OP 90 Amplifier Chips to Board
Testing
Electrical signals generated from
various muscle movements such as
Blinking
 Holding eyes closed
 Heartbeat
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Examination of Noise Artifacts
Sensitivity of Electrode Leads
 Head Movements
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Results - Blinking
Old Amplifier
New Amplifier
Results - Heartbeat
Old Amplifier
New Amplifier
Results - Noise
Old Amplifier
New Amplifier
Problems Encountered
Noise Waveforms
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Head Movement, Touching Electrodes
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Reduce by Twisting Wires Together, so that Random
Movements are Common to the Differential Inputs
Cannot Eliminate All the Noise Output, so Handle
Amplifier with Care
Oscillation of Output Signals
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Caused by 60 Hz Electrical Source, E.g., Lamp
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Suggested Solving by Testing in the Dark or Operate
circuit within an isolated black box
Oscillation Disappeared without Conscious Effort
Cleaner Output
More Results
Screen Captures from DriverLINX
Software
No Processing done Using Keithley Card
 However, Waveform was Successfully
Transmitted to DriverLINX Screen
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MATLAB Simulations
Data was Properly Digitized Using MATLAB
 MATLAB Functions could be Implemented
Using DSP Chips for Memory, Adding,
Squaring, etc
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DriverLINX Capture
Quiet Line
Four Blinks
MATLAB Processing
Function Created for Digital Processing
of the Signal
Captured Data from Oscilloscope
Screen after Blinking Sequences
Concatenated the input Data because
Oscilloscope saved only one screen at
a time
Output Waveform was Purely Digital
Signal Digitization
Before Digitization
After Digitization
Conclusions
Amplifier Functions Acceptably
MATLAB Simulations Illustrate Intended
Implementation of Amplifier
Future Groups may Investigate Alternative
Methods of Applying the Amplifier
Potential Applications
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Wheelchair Control- motions of a wheelchair are
manage without using the hands
On-screen Mouse Control- Move mouse pointer
using facial muscles alone