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, PhDAssistant Professor, Department of Mechanical EngineeringUniversity of IdahoThis majority of this work was completed at theDepartment of Mechanical and Aerospace EngineeringUniversity of California, IrvineSupported by NIH N01-HDand 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-WREXIt 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-ManusImpedance controlImpedance channeltoward targetARM Guide (UCI)Proportional derivative control,Active ConstrainedMIME (Stanford)Proportionalderivative control,Active constrainedand bilateral modes
5 2nd Generation Devices for Movement Training after Stroke Vertical Modulefor MIT-ManusImpedance control &gravity compensationARMin (Zurich)PD control & gravitycompensationRUPERT(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.)+ sensorsWREX+ actuatorsT-WREXPneu-WREX
7 Why Choose Pneumatics? Advantage of Pneumatics Large power to weight ratioClean, and inexpensive.Force controllable.Backdrivable and compliant.Inherent compliance increases safety.Disadvantages of PneumaticsNon-linear frictionRequire advanced controlNot 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 FeaturesSpring 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 PFC4 angular potentiometers, Midori CP-2fb2, 2-axes MEMS accelerometers, Analog Devices ADXL320EB8 pressure sensors,HoneywellASCX100AM
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 bit1 kHz sampling rateTarget Execution Time (TET) ≈ 650 μsA/D, D/A PCI Card, Measurement Computing PCIM-DAS1602/16D/A PCI Card, Measurement Computing PCIM-DDA08/16
12 State EstimationState Estimation using MEMS accelerometers in a Kalman FilterEstimated velocity and position signals have reduced noise and phase lag comparedto a conventionallow-pass filter.
13 State Estimation: Advantages Signals have less noise and less phase lagImproved stabilityQuieter operationReduced air consumption
14 Servovalve Characterization for Improved Force Control Experimentally determinedflow map equation to linearizeairflow 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 TypeHelp Complete MovementsMechanically CompliantAssistAsNeededStiff Proportional Derivative Impedance ControlImpedance Control w/ Gravity OffsetAdaptiveAdaptive, 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. exampleIntroduce 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
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 adaptivecontroller 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
33 SummaryPneu-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 LABORATORIESRobotics & Automation Laboratory, UCI MAEBiomechatronics Laboratory, UCI MAEHuman Performance Laboratory, UCI GCRCCOLLABORATORS (UCI)James Bobrow, Ph.D. Robert Smith, Eng. Tech.Dave J. Reinkensmeyer, Ph.D. Vicki Chan, PTSteven 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.
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: