1. Optimizing Compliant, Model-Based Robotic Assistance to Promote Neurorehabilitation
Eric Wolbrecht, PhD
Assistant Professor, Department of Mechanical Engineering
University of Idaho
This majority of this work was completed at the
Department of Mechanical and Aerospace Engineering
University of California, Irvine
Supported by NIH N01-HD
and NCRR M01RR00827

2. Motivation for Robotic Movement Training
A stroke is suffered by over 700,000 people in the U.S. each year, making it a leading cause of severe, long term disability.
80 percent of stroke victims experience upper extremity movement impairment.
The estimated direct and indirect cost of stroke for 2007 is $62.7 billion.
Stoke rate to increase as population ages.

3. Can Robotic Devices Help?
Evidence suggests that intensive, repetitive sensory motor training can improve functional recovery.
Traditional hands-on therapy is expensive and labor intensive, and therefore patients receive limited amounts of it.
One possible solution to this problem is to develop robotic devices to automate functional motor training.
Robotic movement training with Pneu-WREX
It was previously believed that movement recovery was possible only for acute patients (< 6 months post-stroke). Research has shown that recovery is possible for people with chronic stoke as well (>6 months post-stroke).

4. 1st Generation Devices for Movement Training after Stroke
MIT-Manus
Impedance control
Impedance channel
toward target
ARM Guide (UCI)
Proportional derivative control,
Active Constrained
MIME (Stanford)
Proportional
derivative control,
Active constrained
and bilateral modes

5. 2nd Generation Devices for Movement Training after Stroke
Vertical Module
for MIT-Manus
Impedance control &
gravity compensation
ARMin (Zurich)
PD control & gravity
compensation
RUPERT
(Arizona State)
Open loop control

6. Pneu-WREX: Development History
An offspring of WREX (Wilmington Robotic Exoskeleton), a passive gravity balancing orthosis (Rahman et al, 2000.)
WREX was modified to create T-WREX (Training-WREX), a sensorized, passive gravity balancing orthosis (Sanchez et al, 2004.)
Pneu-WREX (Pneumatic-WREX) was created by adding pneumatic actuators to T-WREX (Sanchez, Wolbrecht et al, 2005.)
Current research focuses on promoting recovery through advanced control (Wolbrecht et al, 2006, 2007.)
+ sensors
WREX
+ actuators
T-WREX
Pneu-WREX

7. Why Choose Pneumatics? Advantage of Pneumatics
Large power to weight ratio
Clean, and inexpensive.
Force controllable.
Backdrivable and compliant.
Inherent compliance increases safety.
Disadvantages of Pneumatics
Non-linear friction
Require advanced control
Not all facilities have compressed air and in-room compressors can be expensive and noisy.

8. Design Features 4 degrees-of-freedom, lightweight, compliant.
Strong (can apply > 50 N of force at hand).
Grip handle with grip sensor.
Two servovalves per cylinder, keeping air consumption and friction low.
Uses a spring to counterbalances the weight of the orthosis, expanding the vertical force range.
Servovalves (2 per cylinder)
Spring Counterbalance Mechanism

9. Safety Features
Spring counterbalance provides a safe transition during an e-stop.
Normally exhausting main valve controls system air supply and is vented during emergency-stop or a detected failure.
Pneumatics are inherently compliant and maximum force is limited.
Workspace of device is less than the workspace of the arm.
Numerous software checks, including a check of redundant position sensors.
Pneu-WREX

10. Sensing 2, 2-axes MEMS accelerometers, Analog Devices ADXL320EB
4 cylinders with LTR potentiometers, Bimba PFC
4 angular potentiometers, Midori CP-2fb
2, 2-axes MEMS accelerometers, Analog Devices ADXL320EB
8 pressure sensors,
Honeywell
ASCX100AM

11. Data Acquisition and Control
Controller developed in The Mathworks Simulink® and executed using the xPC Target real-time operating system.
Data input and output using four Measurement Computing PCI cards
(3) PCIM-DAS1602/16, 8 Differential A/D, 2 D/A, 16 bit
(1) PCI-DDA08/16, 8 channel D/A, 16 bit
1 kHz sampling rate
Target Execution Time (TET) ≈ 650 μs
A/D, D/A PCI Card, Measurement Computing PCIM-DAS1602/16
D/A PCI Card, Measurement Computing PCIM-DDA08/16

12. State Estimation
State Estimation using MEMS accelerometers in a Kalman Filter
Estimated velocity and position signals have reduced noise and phase lag compared
to a conventional
low-pass filter.

13. State Estimation: Advantages
Signals have less noise and less phase lag
Improved stability
Quieter operation
Reduced air consumption

14. Servovalve Characterization for Improved Force Control
Experimentally determined
flow map equation to linearize
airflow through the servovalves.
Separate maps for both inflow and outflow.

15. Hypothesis: An “Optimal” Movement Training Controller Should:
Help Complete Movements. Stimulate afferent signals from the arm by assisting patients in making spatial movements with small errors, overcoming gravity, tone, and weakness.
Be Mechanically Compliant. Allow patients to influence movements, maintaining the effort and error connection essential for motor learning.
Assist Only As Needed. Stimulate efferent signals from the brain by encouraging subjects to contribute as much as possible to the movements.

16. Selecting a Controller for “Optimal” Movement Training
Controller Type
Help Complete Movements
Mechanically Compliant
Assist As
Needed
Stiff Proportional Derivative
   
Impedance Control
Impedance Control w/ Gravity Offset 
Adaptive
Adaptive, Assist-As-Needed

17. Adaptive, Assist-As-Needed Controller
“Adaptive, Assist-As-Needed Controller” builds on the passivity based adaptive controller developed by Slotine and Li.

18. Neuromuscular Weakness Model
For a standard adaptive controller:
For movement training following stoke, however,
must include a general representation of neuromuscular weakness, which is implemented using a grid of radial basis functions in task space:
Radial basis functions 1 D.O.F. example
Introduce the sliding surface and reference trajectory. “Standard Adaptive Control”
There are 120 radial basis functions,
spaced 10 cm apart in a 3-D grid
-8 points left to right (-x to +x)
-5 points in and out (-y to +y)
-3 points down and up (-z to +z)
3-D grid

19. Pneu-WREX
Yes, but, does it work?

20. Testing the Adaptive Controller
Testing Goal: Evaluate controller with and without forgetting to determine how well the controller “assists-as-needed”.
We have tested the adaptive
controller with 8 subjects with movement impairment due to stroke.
1st Test: The subjects tracked a cursor from a central home position to seven targets located in the frontal plane. The cursor (displayed on the computer screen) moved between targets and the home position with a peek velocity of 0.12 m/s.
2nd Test: Subjects tracked a curser between two targets spaced 30 cm apart in the frontal plane (peek velocity 0.12 m/s).

21. Controller Helps Complete Movements
No adaptation. Adapts to different arm weights.

22. Controller Learns Assistance Force for Different Arm Weights
No adaptation. Adapts to different arm weights.

23. With “Forgetting”, Controller Reduces Force when Errors are Small
Drastic difference in robot effort with forgetting, while tracking similar.

24. Without “Forgetting”, Subject Allows Robot to “Take-Over”

25. With “Forgetting”, Subject Contribution Increases

26. With “Forgetting”, Assistance is Proportional to Impairment

27. Therapy Games
Shopping
Egg Cracking
Basketball
Window Cleaning
Driving

28. Therapy with Robotic Assistance
Point to point reaching “Shopping” game, with robotic orthosis assistance.

29. Playing without Robotic Assistance
Point to point reaching “Shopping” game, without robotic orthosis assistance.

30. Adaptive, Assist-As-Needed Controller
Forces applied during point to point reaching “Shopping” game.

31. Robotic Assessments: Game Time

32. Robotic Assessments: Reaching Speeds

33. Summary
Pneu-WREX is a lightweight, compliant, 4 degree-of-freedom upper extremity robotic orthosis.
The adaptive, assist-as-needed controller encourages patient effort while helping the patient to complete movements with small errors.
Subjects feel in control of movements because of compliance, adaptation, and forgetting rate.
We have shown human motor control effort minimization for a real movement trajectory.
Initial pilot therapy study results are promising.

34. Acknowledgements SUPPORT NIH N01-HD-3-3352 and NCRR M01RR0082
LABORATORIES
Robotics & Automation Laboratory, UCI MAE
Biomechatronics Laboratory, UCI MAE
Human Performance Laboratory, UCI GCRC
COLLABORATORS (UCI)
James Bobrow, Ph.D. Robert Smith, Eng. Tech.
Dave J. Reinkensmeyer, Ph.D. Vicki Chan, PT
Steven Cramer, M.D., Ph.D. Vu Le, M.S.
Robert Sanchez, Ph.D. Julius Klein, M.S.
John Leavitt, Ph.D. Koyiro Minakata, B.S.

35. Appendix

36. Appendix: System Dynamics
The system dynamics are
The sliding surface and reference trajectories are defined

37. Appendix: System Dynamics
Using the system dynamics are
Now define
To get
Now substitute for the estimate error and force error
To get

38. Appendix: Force Dynamics
The force dynamics for the base side chambers are
Now substitute for the leakage estimate error
To get

39. Appendix: Lyapunov Function Candidate

40. Force Dynamics and Chamber Force Selection
Cylinder Force Output
Force Dynamics
Chamber force selection with smoothing function

41. Force Controller Passivity based force controller
Adaptive leakage estimation

42. Single Cylinder Force Tracking to 40 Hz

43. Position Tracking to 2 Hz

44. Position Testing Results

45. Controller Modification: Assist-As-Needed
When errors are small, the controller should decay force according to:
The minimum solution for is found by solving a constrained minimization problem:
Introduce the sliding surface and reference trajectory. “Standard Adaptive Control”
The solution is: