1. A Low-Cost Framework for Individualized Interactive Telerehabilitation Chetan Jadhav Advisor: Dr. Venkat Krovi Mechanical and Aerospace Engineering Department State University of New York at Buffalo

2. Slide 2 Chetan Jadhav July 7 th, 2004 Presentation Overview Motivation & Background Description of Framework Implementation Biomechanical Parameter Identification Exercise Assistance Conclusion & Future Work

3. Slide 3 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Motivation Stroke Statistics [1] U.S. alone, each year over 737,000 people experience a new or recurrent stroke $17 billion direct cost (hospitals, physicians, rehabilitation etc.) $13 billion indirect cost (lost productivity etc.) Increase of 31.3% in number of cases from 1979 to 2000 Severely disrupts activities of daily living Suitable motor-rehabilitation regimen can facilitate significant functional recovery [2]

4. Slide 4 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Rehabilitation Therapy Requirements Early and accurate diagnosis of the disease coupled with careful characterization of the level of functional impairment Functional recovery linked to the duration, frequency, regularity and intensity of therapy Attention and monitoring required from a therapist Trends Computer-enhanced Therapy Home based Therapy

5. Slide 5 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Computer-enhanced Rehabilitation Therapy Computerized Exercise Systems Leverages the ubiquitous computational power Measures and records patients performance Interacts and adjusts the therapy regimen Robotic therapy devices Allows more complex therapies like Constraint Induced Therapy [3] Guides the patient through the intensive, repetitive practice of functional movement

6. Slide 6 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Example of Computer-enhanced Rehabilitation MIT MANUS [4] A planar, two-revolute-joint robot Assists patients in sliding their arms across a tabletop Significant improvements in motor recovery Measurement tool to track disease progress

7. Slide 7 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Limitations of Computer-enhanced Rehabilitation Suitable for outpatient units Specialized device and not readily available Therapists or experts interventions is required Prohibitive cost for personal use

8. Slide 8 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Home Based Rehabilitation Flexibility in intensity and duration of rehabilitation regimen Comparable effects with hospital attendance in terms of functional gains [4] Therapist visits the patients home periodically Lack of structured and monitored exercises can mitigate achievable benefits Use of specialized exercise machines is limited Not much cost benefits due to logistic issues associated

9. Slide 9 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Proposed Telerehabilitation Framework

10. Slide 10 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Research Issues Requirements/Needs Home based Low-cost Use of COTS Automated decision support Quantitative data capture Quantitative assessment of disability and performance Individualized Update exercise based on patient model

11. Slide 11 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Virtual Driving Environment Not just a driving simulator or trainer Illustrative example to integrate the multiple facets of telerehabilitation Helps identify the issues with development, implementation and deployment Serves to enhance one higher activities of daily living

12. Slide 12 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Contemporary Telerehabilitation Use video conferencing technology High bandwidth internet connection required Multiple camera views are necessary to recognize patients movement patterns Lack of quantitative assessment capabilities Suitable for outpatient units Prohibitive cost for home use

13. Slide 13 Chetan Jadhav July 7 th, 2004 Focus of this Thesis Part AImplementation of VDE Part BBiomechanical Identification Part CManipulation Assist

14. Slide 14 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Hardware Force Feedback (FFB) Gamming Wheel Rate-Gyro Part A

15. Slide 15 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Implementation Within MATLAB/Simulink environment Various toolboxes available Application Program Interface (API) to extend capabilities Fast prototyping environment Part A

16. Slide 16 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Software MATLAB FFB wheel interface Does not posses ability to access such devices Implemented using API, C++, DirectX Part A

17. Slide 17 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Software (cont.) TCP/IP implementation Connects JACK to MATLAB application JACK model can be controlled from MATLAB to recreate exercise session Winsock2 libraries used in S-Function Part A

18. Slide 18 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Software (cont.) User interfaces Two-D Dial Implementation Fully Immersive VE Part A

19. Slide 19 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Biomechanical Parameter Identification Assessing performance of movement tasks Background Muscle activation or Electro Myographs (EMG) Frequency or time normalization Pathological motor patterns identified by comparison Bilateral comparison between limb Joint kinetics Complexity of modeling Computational Tools like SIMM and Anybody need to be customized to match the specific individual Joint kinematics Forms a sound basis for kinetic model Visualization needs at Therapist Interface Part B Quantitative data capture Quantitative performance Individualized

20. Slide 20 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Upper-limb Kinematic Model Part B

21. Slide 21 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Calibration of Planar Four-Bar Mechanism Used techniques from ROBOTICS to identify link lengths Simulated using known four-bar mechanism Part B

22. Slide 22 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Calibration of Planar Four-Bar Mechanism (cont.) Taylor series expansion of is, For k measurements Solving above system by pseudoinverse method Part B

23. Slide 23 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Calibration of Planar Four-Bar Mechanism (cont.) Considering the use of COTS components, random noise simulated 5%10%25% Lengths% Length Error (5 % Noise) % Length Error (10 % Noise) % Length Error (25 % Noise) l3l3 4-24 l4l4 0.36 Iterations4399103 Part B

24. Slide 24 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Calibration of Spatial Four-Bar Mechanism Shoulder approximated to spherical Elbow approximated as universal Wrist and grip approximated as single universal Steering joint is revolute DOF = 2 Part B

25. Slide 25 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Calibration of Spatial Four-Bar Mechanism (cont.) Two-Link trial Fast convergence (3 iterations) ParameterInitialCalibratedActual% Error L1L1 1.29910.59420.60.96 L2L2 0.50.99721.00.28 Part B

26. Slide 26 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Manipulation Assist Background Classification based on use of equipment Unassisted Exercises Machine Assisted Exercises Robotic-Assisted Exercises Classification based on Nature of Assist Passive Resist Active Resist Classification based on Type of Manipulator Assist Motion Assistance Force Assistance Combined Motion Force Assistance Part C Quantitative data capture Quantitative performance Individualized

27. Slide 27 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Exercise Assistance Model of the interaction of the patient with the driving wheel and the virtual environment Part C

28. Slide 28 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Exercise Assistance (cont.) Dynamics of the system can be written as, In state-space form Where states are Part C

29. Slide 29 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Exercise Assistance (cont.) Non-linear system Feedback linearization technique is used Part C

30. Slide 30 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Input-Output Linearization for Motion Assistance Output equation can be written as, The relative degree of the system is three and it is input- state linearizable and the decoupling matrix is, Nonlinear state feedback can be computed as, where, Solving for and Part C

31. Slide 31 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Input-Output Linearization for Motion Assistance (cont.) States are transformed via a diffeomorphism Linearized system is written as, Part C

32. Slide 32 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Control system is designed using pole-placement techniques Stepping back through the diffeormorphism Desired linearized sates ( z d ) can be calculated using diffeomorphism Input-Output Linearization for Motion Assistance (cont.) Part C

33. Slide 33 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Simulation Results Input-Output Linearization for Motion Assistance (cont.) Part C

34. Slide 34 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Output equation can be written as, The relative degree of the system is three and it is input- state linearizable We set y=0, then θ 3 = 0 and associated zero dynamics is, which is asymptotically stable The control law may be computed as, Input-Output Linearization for Force Assistance Part C

35. Slide 35 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 k is chosen such that the polynomial K(s) = s+k has all its roots in left half plane. If we let k = -1, then, The control law can be written as, Input-Output Linearization for Force Assistance (cont.) Part C

36. Slide 36 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Simulation Results Input-Output Linearization for Force Assistance (cont.) Part C

37. Slide 37 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Conclusion Successful implementation Efficient Kinematic Calibration method for biomechanical parameter identification Motion and Force assistance using non-linear control

38. Slide 38 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Future Work Global calibration method which can take arbitrary parameters as initial guesses Recursive least square for on-going calibration Real time implementation in Windows environment

39. Slide 39 Chetan Jadhav July 7 th, 2004 Questions ?

40. Slide 40 Chetan Jadhav July 7 th, 2004 Thank You

41. Slide 41 Chetan Jadhav July 7 th, 2004 References [1]Anon. (2003). National Stroke Association. Available: http://www.stroke.org [2]P. Langhorne, R. Wagenaar, and C. Partridge, "Physiotherapy after stroke: More is better?," Physiother Res Int, vol. 1, no. 2, pp. 75-88, 1996. [3]S. L. Wolf, D. E. Lecraw, L. A. Barton, and B. B. Jann, "Forced use of hemiplegic upper extremities to reverse the effect of learned nonuse among chronic stroke and head-injured patients," Exp Neurol, vol. 104, no. 2, pp. 125-32, 1989. [4]M. L. Aisen, H. I. Krebs, N. Hogan, F. McDowell, and B. T. Volpe, "The effect of robot-assisted therapy and rehabilitative training on motor recovery following stroke," Arch Neurol, vol. 54, no. 4, pp. 443-6, 1997. [5]J. B. Young and A. Forster, "The bradford community stroke trial: Results at six months," Bmj, vol. 304, no. 6834, pp. 1085-9, 1992.

42. Slide 42 Chetan Jadhav July 7 th, 2004 Virtual Patient Model Back

43. Slide 43 Motivation Framework Implementation Identification Assistance Conclusion Chetan Jadhav July 7 th, 2004 Patient Interface Back

44. Slide 44 Chetan Jadhav July 7 th, 2004 Steering Interface with MATLAB Back

45. Slide 45 Chetan Jadhav July 7 th, 2004 Matlab Jack Interface Back

46. Slide 46 Chetan Jadhav July 7 th, 2004 VDE Back