Real-Time Control of a Multi-Fingered Robot Hand Using EMG Signals

1. SDSU Real-Time Control of a Multi-Fingered Robot Hand Using EMG Signals Master’s Thesis By Luenin Barrios Supervisor: Marko Vuskovic Department of Computer Science San Diego State University June 29, 2010

2. SDSU Outline • Introduction to Research • Multi-Fingered Robot Hands and Prostheses • Measurement of EMG Signals • Feature Extraction and Classification • Synergy and Robot Control System • Hardware Description • Implementation • Observations and Results • Summary

3. SDSU Introduction • Goal of Research: To implement a program that uses the EMG Classifier output to control the grasp motions of the SDSU robot hand in real-time • Grasp modes: Chris Miller Master’s Thesis 2008.

4. SDSU Prosthetic Hands Overview • Early Models • Restrictions and Limitations • Degrees of Freedom • EMG Signal Control Otto Bock Grasp Pincher TAP Version 3 Prototype SDSU Robot Hand

5. SDSU Overall Schematic Saksit Siriprayoonsak 2005 This Project Chris Miller 2008

6. SDSU EMG Signals • Electromyography • EMG potentials: 50 μV and up to 20 to 30 mV Source:www.univie.ac.at/cga/courses/be522/emg/fiber.gif

7. SDSU Forearm Muscle Anatomy Chris Miller Master’s Thesis 2008

8. EMG Amplifier Device • Saksit Siriprayoonsak 2005 4 Bipolar Channels 1 Reference Channel Surface Electrodes

9. SDSU EMG Amplifier Device Con’td

10. SDSU EMG Classifier ProgramSignal Detection • Bonato Method • Onset of Movement

11. SDSU ClassifierSignal Processing • Feature Extraction Methods: Waveform Length (Farry et al., 1996) Spectral Moments (Vuskovic et al., 2005)

12. SDSU EMG Signal Processing Feature Extraction Method 1 Waveform Length

13. SDSU EMG Signal ProcessingFeature Extraction Method 2 • Spectral Moments • I-coefficients

14. SDSU Feature Classification • Mahalanobis Distance(Mahalanobis, 1936) • Sample Feature Vector Space

15. SDSU Feature Log Transformation • Box Cox Transformation (1964)

16. SDSU Robot Joint Control System • PID Controller and Actuator

17. SDSU Joint Control System • Acutuator Model

18. SDSU Joint Control System • PID Controller e = qmd - qm; // Get controller error qmdot = (qm-qmold)/_Ts; // Get derivative of error ei = eiold + e * _Ts; // Get integral of error u = _Kp*e - _Kv*qmdot + _Ki*ei; // Control law qmold = qm; eiold = ei;

19. SDSU Synergetic Motion • Synergetic Mapping θj = fj(m, D) where j = 0, 1…5 and m = 1…4   • Approximation Function (Vuskovic and Marjanski) cm,j = αm,j am,j = γm,j

20. SDSU Synergetic Training Joint Angle θ1 (cm,1 + D1) = am,1 bm,1 - am,1 D1

21. Object Shapes and Sizes Spherical Point Cylindrical Lateral

22. SDSU Calibration and Training Sample Training for Point Objects: Sample Positions for Lateral, Cylindrical and Spherical

23. SDSU Robot Hardware • Servo To Go Board • Signal Transition Box

24. SDSU Servo To Go Interface Board • Encoder Input A/B signal • Analog Input/Output

25. SDSU Signal Transition Box • Central hub for signals/cables • Relays information Example: Joint 0 P3 DAC 2 EnIn A 14 EnIn B 17 DB50 EnOut A 35 EnOut B 34 DB25 AnalogIn 2

26. SDSU EMG Robot Hand • User Interface/Motion Command Interpreter • Client/Server • TCP/IP • Real-time EMG/User Commands • Grasp modes: Cylindrical, Spherical, Point, Lateral

27. SDSU EMG Robot Hand Cont’d Examples: Command: g 0 45 Command: o 3 9

28. SDSU Overall Runtime Flow Chart

29. SDSU Overall Runtime Flow Chart Cont’d

30. SDSU System Execution Step 2 Step 3 Step 1 Step 4

31. SDSU Summary • Multi-fingered Robot Hands and EMG Signals • Collection of EMG Signals • Feature Extraction and Classification • PID Controller • Synergetic Motion • Overall System Diagram and Transition Box • Real-time control of Robot Hand using EMG Signals

32. Conclusions/Future Work • Feasibility of EMG Signal for Hand Control • Synergetic Grasp Motions • Classifier for real-time control • Combine projects so they reside on same machine • Improve arm/amplifier device contact • Wireless electrodes/sensory network • Improve time delays in Classifier

33. SDSU Questions/Comments?