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Optimizing Compliant, Model-Based Robotic Assistance to Promote Neurorehabilitation Eric Wolbrecht, PhD Assistant Professor, Department of Mechanical Engineering.

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Presentation on theme: "Optimizing Compliant, Model-Based Robotic Assistance to Promote Neurorehabilitation Eric Wolbrecht, PhD Assistant Professor, Department of Mechanical Engineering."— Presentation transcript:

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).

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

5 2 nd Generation Devices for Movement Training after Stroke ARMin (Zurich) PD control & gravity compensation Vertical Module for MIT-Manus Impedance 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.) WREX 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 Spring Counterbalance Mechanism Servovalves (2 per cylinder) 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. 4 degrees-of-freedom, lightweight, compliant. Strong (can apply > 50 N of force at hand). Grip handle with grip sensor.

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

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: 1.Help Complete Movements. Stimulate afferent signals from the arm by assisting patients in making spatial movements with small errors, overcoming gravity, tone, and weakness. 2.Be Mechanically Compliant. Allow patients to influence movements, maintaining the effort and error connection essential for motor learning. 3.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 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. 1 st 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. 2 nd 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

22 Controller Learns Assistance Force for Different Arm Weights

23 With “Forgetting”, Controller Reduces Force when Errors are Small

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 ShoppingEgg CrackingBasketball Window CleaningDriving

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 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 To get Now define 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 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: The solution is:

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