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Real-Time Control of a Multi-Real-Time Control of a Multi-Fingered Robot Hand Using Fingered Robot Hand Using
EMG SignalsEMG SignalsMaster’s ThesisMaster’s Thesis
ByBy
Luenin BarriosLuenin Barrios
Supervisor: Marko VuskovicSupervisor: Marko Vuskovic
Department of Computer ScienceDepartment of Computer ScienceSan Diego State UniversitySan Diego State University
June 29, 2010June 29, 2010
SDSU
OutlineOutline Introduction to ResearchIntroduction to Research Multi-Fingered Robot Hands and Multi-Fingered Robot Hands and
ProsthesesProstheses Measurement of EMG SignalsMeasurement of EMG Signals Feature Extraction and ClassificationFeature Extraction and Classification Synergy and Robot Control SystemSynergy and Robot Control System Hardware DescriptionHardware Description Implementation Implementation Observations and ResultsObservations and Results SummarySummary
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IntroductionIntroduction Goal of Research:Goal of Research:
To implement a program that uses To implement a program that uses the EMG Classifier output to control the EMG Classifier output to control the grasp motions of the SDSU robot the grasp motions of the SDSU robot hand in real-timehand in real-time
Grasp modes: Grasp modes:
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Chris Miller Master’s Thesis 2008.
Prosthetic Hands Prosthetic Hands OverviewOverview
Early ModelsEarly Models Restrictions and Restrictions and
LimitationsLimitations Degrees of FreedomDegrees of Freedom EMG Signal ControlEMG Signal Control
Otto Bock Grasp Pincher TAP Version 3 Prototype SDSU Robot Hand
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Overall SchematicOverall Schematic
Prosthetic Hand
Controller
EMG Amplification
Device
A/D Converter
Classifier
Signal Processing
Time Sample Extraction
Feature Extraction
Transformation
Saksit Siriprayoonsak 2005
Chris Miller 2008
This Project
SDSU
EMG SignalsEMG Signals ElectromyographyElectromyography EMG potentials: 50 μV and up to 20 to 30 mVEMG potentials: 50 μV and up to 20 to 30 mV
Source:www.univie.ac.at/cga/courses/be522/
emg/fiber.gif
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Forearm Muscle Forearm Muscle AnatomyAnatomy
Chris Miller Master’s Thesis 2008
SDSU
EMG Amplifier DeviceEMG Amplifier Device
Saksit Siriprayoonsak Saksit Siriprayoonsak 20052005
4 Bipolar Channels4 Bipolar Channels
1 Reference Channel1 Reference Channel
Surface ElectrodesSurface Electrodes
EMG Amplifier Device EMG Amplifier Device Con’tdCon’td
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EMG Classifier ProgramEMG Classifier ProgramSignal DetectionSignal Detection
Bonato MethodBonato Method Onset of Onset of
MovementMovement
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ClassifierClassifierSignal ProcessingSignal Processing
Feature ExtractionFeature Extraction
Methods:Methods:
Waveform Length (Farry Waveform Length (Farry et alet al., ., 1996)1996)
Spectral Moments (Vuskovic Spectral Moments (Vuskovic et et alal., 2005)., 2005)
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EMG Signal Processing EMG Signal Processing Feature Extraction Method Feature Extraction Method
11Waveform Length
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EMG Signal ProcessingEMG Signal ProcessingFeature Extraction Method Feature Extraction Method
22 Spectral MomentsSpectral Moments
I-coefficientsI-coefficients
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Feature ClassificationFeature Classification Mahalanobis Distance(Mahalanobis, 1936)Mahalanobis Distance(Mahalanobis, 1936)
Sample Feature Vector Sample Feature Vector SpaceSpace
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Feature Log Feature Log TransformationTransformation
Box Cox Transformation (1964)Box Cox Transformation (1964)
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Robot Joint Control Robot Joint Control SystemSystem
PID Controller and Actuator PID Controller and Actuator
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Joint Control SystemJoint Control System Acutuator ModelAcutuator Model
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Variable Description Unit
Ra Terminal or Armature Resistance
3.38 Ohm
Ka Torque Constant 8.11 mNm/A
Jm Rotor Inertia 1.27 gcm2
Kg Gear Transmission Ratio - Thumb
Gear Transmission Ratio - Finger
1:261:19
Ga Driver Gain 1
Kb Speed or Proportionality Constant
1180 rpm/V
V0 Nominal Voltage 12 Volt
ω0 No Load Speed 13900 rpm
Joint Control SystemJoint Control System
PID ControllerPID Controller
e = qmd - qm; // Get controller errorqmdot = (qm-qmold)/_Ts; // Get derivative of errorei = eiold + e * _Ts; // Get integral of erroru = _Kp*e - _Kv*qmdot + _Ki*ei; // Control lawqmold = qm;eiold = ei;
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Synergetic MotionSynergetic Motion Synergetic MappingSynergetic Mapping
θθjj = = ffjj (m, D)(m, D) where where jj = 0, 1…5 and = 0, 1…5 and mm = = 1…4 1…4
Approximation Function (Vuskovic and Approximation Function (Vuskovic and Marjanski)Marjanski)
am,j = γm,jcm,j = αm,j
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Synergetic TrainingSynergetic TrainingJoint Angle
θ1 (cm,1 + D1) = am,1 bm,1 - am,1 D1
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Object Shapes and SizesObject Shapes and SizesSpherical Point
Cylindrical Lateral
Calibration and TrainingCalibration and TrainingSDSU
Sample Training for Point Objects:
Sample Positions for Lateral, Cylindrical and Spherical
Robot HardwareRobot Hardware
Servo To Go BoardServo To Go Board Signal Transition Signal Transition
BoxBox
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Servo To Go Interface Servo To Go Interface BoardBoard
Encoder Input A/B signalEncoder Input A/B signal Analog Input/OutputAnalog Input/Output
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Signal Transition BoxSignal Transition Box Central hub for signals/cablesCentral hub for signals/cables Relays information Relays information Example: Joint 0
P3 DAC 2 EnIn A 14 EnIn B 17
DB50 EnOut A 35 EnOut B 34
DB25 AnalogIn 2
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EMG Robot HandEMG Robot Hand User Interface/Motion Command InterpreterUser Interface/Motion Command Interpreter Client/ServerClient/Server TCP/IPTCP/IP Real-time EMG/User CommandsReal-time EMG/User Commands Grasp modes: Cylindrical, Spherical, Point, LateralGrasp modes: Cylindrical, Spherical, Point, Lateral
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EMG Robot Hand Cont’dEMG Robot Hand Cont’dExamples: Examples:
Command: g 0 45Command: g 0 45
Command: o 3 9
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Overall Runtime Flow Overall Runtime Flow ChartChart
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Overall Runtime Flow Chart Overall Runtime Flow Chart Cont’dCont’d
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System ExecutionSystem Execution
Step 1
Step 2 Step 3
Step 4
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SummarySummary Multi-fingered Robot Hands and EMG Multi-fingered Robot Hands and EMG
SignalsSignals Collection of EMG SignalsCollection of EMG Signals Feature Extraction and ClassificationFeature Extraction and Classification PID ControllerPID Controller Synergetic MotionSynergetic Motion Overall System Diagram and Transition BoxOverall System Diagram and Transition Box Real-time control of Robot Hand using EMGReal-time control of Robot Hand using EMG SignalsSignals
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Conclusions/Future WorkConclusions/Future Work
Feasibility of EMG Signal for Hand Feasibility of EMG Signal for Hand ControlControl
Synergetic Grasp MotionsSynergetic Grasp Motions Classifier for real-time controlClassifier for real-time control Combine projects so they reside on Combine projects so they reside on
same machinesame machine Improve arm/amplifier device contactImprove arm/amplifier device contact Wireless electrodes/sensory networkWireless electrodes/sensory network Improve time delays in ClassifierImprove time delays in Classifier
Questions/Comments?Questions/Comments?
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