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Group N3: Digital Control


Title: Purpose Author: School of ECE Last modified by: School of ECE Created Date: 4/23/2002 5:09:17 PM Document presentation format: On-screen Show – PowerPoint PPT presentation

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Title: Group N3: Digital Control

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
  • Biofeedback
  • Mainly for clinical purposes
  • Utilized as muscle relaxation and treatment
  • Also to prevent painful muscle conditions
  • Communication
  • 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

Bodily Electrical Activity
  • Contraction of all muscles is triggered by
    electrical impulses called Motor Unit Action
    Potentials (MUAPs)
  • MUAPs 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
  • 50 500 Hz
  • Voltage Range
  • 0.75 2000 uV

Electromyography II
  • Underlying Processes
  • Differential Amplification
  • Signal-to-Noise Ratio (SNR)
  • Common Mode Rejection Ratio (CMRR)
  • 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
  • Build and test EMG Circuit from Last Semester
  • Acquire a transferable A/D converter
  • Back End
  • Determine best way to signal the Remote Control

Interfacing to PC
  • Digitize Signals Measured with EMG
  • Analog to Digital Converter
  • Comparator
  • Assign Codes for System Control
  • Determine Number and Degree of Muscle Movements
    to Execute Commands
  • Apply Commands on Test System

Initial Design Issues
  • EMG Circuit
  • Will previous design work
  • Signal Source Usage
  • Which muscles to monitor
  • A/D Converter Software
  • 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
  • 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
  • Good contraction speed
  • Difficult to move one eyebrow
  • Low fat tissue coverage
  • Jaw muscles
  • 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
  • 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
  • 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
  • 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
  • Had To Utilize Adapters to Connect Surface-Mount
    OP 90 Amplifier Chips to Board

  • Electrical signals generated from various muscle
    movements such as
  • Blinking
  • Holding eyes closed
  • Heartbeat
  • Examination of Noise Artifacts
  • Sensitivity of Electrode Leads
  • Head Movements

Results - Blinking
  • Old Amplifier
  • New Amplifier

Results - Heartbeat
  • Old Amplifier
  • New Amplifier

Results - Noise
  • Old Amplifier
  • New Amplifier

Problems Encountered
  • Noise Waveforms
  • Head Movement, Touching Electrodes
  • 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
  • Caused by 60 Hz Electrical Source, E.g., Lamp
  • 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
  • MATLAB Simulations
  • Data was Properly Digitized Using MATLAB
  • MATLAB Functions could be Implemented Using DSP
    Chips for Memory, Adding, Squaring, etc

DriverLINX Capture
  • Quiet Line
  • Four Blinks

MATLAB Processing
  • Function Created for Digital Processing of the
  • 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

  • Amplifier Functions Acceptably
  • MATLAB Simulations Illustrate Intended
    Implementation of Amplifier
  • Future Groups may Investigate Alternative Methods
    of Applying the Amplifier
  • Potential Applications
  • Wheelchair Control- motions of a wheelchair are
    manage without using the hands
  • On-screen Mouse Control- Move mouse pointer using
    facial muscles alone