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Transcript of A Low-Cost Framework for Individualized Interactive Telerehabilitation Chetan Jadhav Advisor: Dr....
A Low-Cost Framework for Individualized Interactive Telerehabilitation
Chetan Jadhav
Advisor: Dr. Venkat KroviMechanical and Aerospace Engineering Department
State University of New York at Buffalo
Chetan JadhavJuly 7th, 2004
Slide 2
Presentation Overview
• Motivation & Background• Description of Framework• Implementation• Biomechanical Parameter Identification• Exercise Assistance• Conclusion & Future Work
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 3
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]
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 4
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
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 5
Computer-enhanced Rehabilitation Therapy
• Computerized Exercise Systems• Leverages the ubiquitous computational power
• Measures and records patient’s 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
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 6
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
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 7
Limitations of Computer-enhanced Rehabilitation
• Suitable for outpatient units• Specialized device and not readily available• Therapist’s or expert’s interventions is required• Prohibitive cost for personal use
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 8
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 patient’s 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
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 9
Proposed Telerehabilitation Framework
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 10
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
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 11
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
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 12
Contemporary Telerehabilitation
• Use video conferencing technology• High bandwidth internet connection required• Multiple camera views are necessary to recognize
patient’s movement patterns• Lack of quantitative assessment capabilities• Suitable for outpatient units• Prohibitive cost for home use
Chetan JadhavJuly 7th, 2004
Slide 13
Focus of this Thesis
Part A Implementation of VDE
Part B Biomechanical Identification
Part C Manipulation Assist
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 14
Hardware
Force Feedback (FFB) Gamming Wheel
Rate-Gyro
Part A
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 15
Implementation
Within MATLAB/Simulink environment• Various toolboxes available
• Application Program Interface (API) to extend capabilities
• Fast prototyping environment
Part A
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 16
Software
MATLAB FFB wheel interface• Does not posses ability to access such devices
• Implemented using API, C++, DirectX
Part A
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 17
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
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 18
Software (cont.)
User interfaces
Two-D Dial Implementation
Fully Immersive VE
Part A
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 19
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
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 20
Upper-limb Kinematic Model
Part B
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 21
Calibration of Planar Four-Bar Mechanism
• Used techniques from ROBOTICS to identify link lengths
• Simulated using known four-bar mechanism
2 2 1 1 3 3 4 4
2 2 1 1 3 3 4 4
cos cos cos cos
sin sin sin sinmeasured nominal
l l l lX X X
l l l l
Part B
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 22
Calibration of Planar Four-Bar Mechanism (cont.)
Taylor series expansion of is,
For k measurements
Solving above system by pseudoinverse method
X
3 4 2 2 3 3 4 43 4 2 3 4
3 4 2 2 3 3 4 4
cos cos sin sin sin
sin sin cos cos cos
Tl l lX l l
l l l
1 1
k k
X
X
X
1T TX
Part B
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 23
Calibration of Planar Four-Bar Mechanism (cont.)
Considering the use of COTS components, random noise simulated
0 10 20 30 40 50 60 70-2
-1.5
-1
-0.5
0
0.5
1
1.5
2L3L4
0 10 20 30 40 50 60 70 80 90 100-2
-1.5
-1
-0.5
0
0.5
1
1.5
2L3L4
0 20 40 60 80 100 120-2
-1.5
-1
-0.5
0
0.5
1
1.5
2L3L4
5% 10% 25%
Lengths % Length Error (5 % Noise)
% Length Error (10 % Noise)
% Length Error (25 % Noise)
l3 4 -2 4
l4 0.3 -1 6
Iterations 43 99 103
Part B
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 24
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
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 25
Calibration of Spatial Four-Bar Mechanism (cont.)
Two-Link trial
• Fast convergence (3 iterations)
Parameter Initial Calibrated Actual % Error
L1 1.2991 0.5942 0.6 0.96
L2 0.5 0.9972 1.0 0.28
Part B
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 26
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
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 27
Exercise Assistance
Model of the interaction of the patient with the driving wheeland the virtual environment
Part C
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 28
Exercise Assistance (cont.)
• Dynamics of the system can be written as,
• In state-space form
• Where states are
1 2 1 1 2 1 1 2 1 act h
J B C
J J B B C C
2
32 1 2
3
1 2 3 1 2 3
0
0
10
, , , ,
C Bu
J J J
f g u
1 2 31 1
Part C
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 29
Exercise Assistance (cont.)
• Non-linear system
• Feedback linearization technique is used
Part C
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 30
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
1
2 1 2 3
3
1 0 0 , ,y h
u
1J
3 and fL h I
1 2 3BC B BC
J J J
J
Part C
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 31
Input-Output Linearization for Motion Assistance (cont.)
• States are transformed via a diffeomorphism
• Linearized system is written as,
2
1 1
2 2
3 3
1 0 0
0 1 0
1
f fz T h L h L h
z
z
z C B
J J J
1 1
2 2
3 3
0 1 0 0
0 0 1 0
0 0 0 1
z z
z z
z z
Part C
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 32
• Control system is designed using pole-placement techniques
• Stepping back through the diffeormorphism
• Desired linearized sates (zd) can be calculated using diffeomorphism
3 3 3 2 2 1 12 1 2
d d d dz K z z K z z K z z
3
2 1 2 3 3 3 2 2 1 12 1 2 d d d d
u
C BC B J z K z z K z z K z z
J J J
Input-Output Linearization for Motion Assistance (cont.)
Part C
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 33
• Simulation Results
Input-Output Linearization for Motion Assistance (cont.)
Part C
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 34
• 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 θ3 = 0 and associated zero dynamics is,
which is asymptotically stable
• The control law may be computed as,
Input-Output Linearization for Force Assistance
1
2 1 2 3
3
0 0 1 , ,y h
1 1 1B C
J J
1f
g
u L h khL h
Part C
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 35
• 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.)
2
31 20 0 1 0
0
f
C BL h
J J J
3 310 ( 1)
1u
3 3 3d dpu K
Part C
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 36
• Simulation Results
Input-Output Linearization for Force Assistance (cont.)
Part C
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 37
Conclusion
• Successful implementation
• Efficient Kinematic Calibration method for biomechanical parameter identification
• Motion and Force assistance using non-linear control
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 38
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
Chetan JadhavJuly 7th, 2004
Slide 41
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.
Chetan JadhavJuly 7th, 2004
Slide 42
Virtual Patient Model
Back
Motivation Framework Implementation Identification Assistance ConclusionChetan JadhavJuly 7th, 2004
Slide 43
Patient Interface
Back
Chetan JadhavJuly 7th, 2004
Slide 44
Steering Interface with MATLAB
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Chetan JadhavJuly 7th, 2004
Slide 45
Matlab Jack Interface
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