Use of Hybrid System Modeling Technique fo r Robot- Assisted Rehabilitation Systems

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Use of Hybrid System Use of Hybrid System Modeling Technique foModeling Technique for r

Robot-Robot-Assisted Rehabilitation Assisted Rehabilitation SystemsSystems

Duygun ErolDuygun Erol

Yeditepe UniversityElectrical & Electronics Engineering Department

Istanbul, TURKEY

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MotivationMotivation Stroke is a highly prevalent condition, especially among the elderly, that results in high costs to the individual and the society

Every year, a large number of people have a stroke in all over the world

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MotivationMotivation Stroke rehabilitation points towards the intense and repetitive movement assisted therapy that has shown significant beneficial impact on a large segment of the patients

The availability of such training techniques, however, are limited by

the amount of costly therapist’s time they involve

the ability of the therapist to provide controlled, quantifiable and repeatable assistance

Robot-assisted rehabilitation that can

provide quantifiable and repeatable assistance that ensure consistency during the rehabilitation

likely to be cost-efficient

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Robot-Assisted Robot-Assisted Rehabilitation SystemsRehabilitation Systems

MIT Manus ARM Guide

GENTLE/sMIME ADLER

Rutgers Master II-ND

Carnegie Mellon Hand

Device

CyberGrasp HWARD

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LimitationsLimitations Two important issues that the current robot-assisted rehabilitation systems do not address

First, they are limited by their inability to simultaneously assist both arm and hand movements

Second, none of these robot-assisted rehabilitation systems can comprehensively alter the task parameters based on patient’s feedback to impart effective therapy during the execution of the task in an automated manner

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Scope of the ResearchScope of the ResearchMovement Assisted Therapy

Task-Oriented Therapy

Robot-Assisted Rehabilitation

Systems forHand Movement


Systems for Arm Movement

Rehabilitation Therapy Approaches


Systems for both Arm and Hand

Movement(?)

The objective of this work is to develop an intelligent control architecture for robot-assisted rehabilitation systems that can provide assistance to both the arm and hand in a coordinated manner to allow

stroke patients to undergo task-oriented active training therapy

Additionally a human intention recognition system is augmented inside the control architecture to dynamically modify the rehabilitation

task based on the patient’s verbal feedback

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Intelligent Control Intelligent Control ArchitectureArchitecture

Hybrid system theory provides mathematical tools that accommodate both continous and discrete system in a unified manner

Hybrid system model provides flexible and extendible environment

Hybrid system allows analysis of properties such as stability, reachability

Advantage of using hybrid system model is taken to model the robot-assisted rehabilitation system

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PLANT PLANT Rehabilitation Robotic SystemRehabilitation Robotic System

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PLANT PLANT (cont’)(cont’)Rehabilitation Robotic SystemRehabilitation Robotic System

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PLANT PLANT (cont’)(cont’)Human Intention RecognitionHuman Intention Recognition

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Task DesignTask Design(Drinking from a Bottle)(Drinking from a Bottle)

i) reach towards the bottle while opening the hand, ii) reach the bottle, iii) close the hand to grasp the bottle, iv) move the bottle towards the mouth, v) drink from a bottle using a straw, vi) place the bottle back on the table, vii) open the hand to leave the bottle back on the table and viii) go back to starting position

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Design Details of theDesign Details of theIntelligent Controller Intelligent Controller

(Theory)(Theory) The discrete event system (DES) plant model (plant and interface) is a nondeterministic finite automaton

G ( P, X ,R, , )

The DES controller (high-level controller) is modeled as a discrete-event system (DES) deterministic finite automaton

D ( S , X ,R, , )

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Design Details of theDesign Details of the Intelligent Controller Intelligent Controller

Each region in the state space of the plant bounded by the hypersurface is associated with a state of the DES plant as:

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Design Details of theDesign Details of the Intelligent Controller (cont’) Intelligent Controller (cont’) The plant symbols G ( P, X ,R, , ) X in

are found using:x[ n] ( p[ n 1], p[ n])

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Design Details of theDesign Details of the Intelligent Controller (cont’) Intelligent Controller (cont’)

D ( S , X ,R, , ) The control statesS in

s[ n] ( s[ n 1],x[ n]) are found using:

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1

2

3

4

5

if r r 1

if r r 2

plant input if r r 3

if r r 4

if r r 0

R The control symbols in

are found using:r[ n] ( s[ n])

D ( S , X ,R, , )

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ExperimentsExperiments First the validation of the human intention recognition system is summarized

Then the results of the experiments to demonstrate the efficacy of the proposed control architecture are provided

The Matlab/Simulink/Stateflow software is used to implement the proposed high-level controller

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ResultsResults (Validation of Human Intention (Validation of Human Intention Recognition SystemRecognition System))

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ResultsResults((Evaluation of the Proposed Intelligent Control Evaluation of the Proposed Intelligent Control

Architecture Architecture ))

Desired Motion Trajectory

0 10 20 30 40 50 60 700

0.05

0.1

0.15

0.2

0.25

0.3

Time (s)

Y-d

irec

tion

(m)

Desired Position

0 10 20 30 40 50 60 700

0,05

0,1

0,15

Desired Position

Time (s)

Z-d

irec

tion

(m)

A

CB

D E

F G

H A B C

D E

F G H

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Control MechanismControl Mechanism

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0 10 20 30 40 50 60 70 80 90 1000

0.05

0.1

0.15

0.2

0.25

0.3

Time (s)

Y-d

irec

tion

(m)

Desired Position

0 10 20 30 40 50 60 70 80 90 1000

0,05

0,1

0,15

Desired Position

Time (s)

Z-d

irec

tion

(m)

With High-Level Controller ModificationWithout High-Level Controller Modification

With High-Level Controller ModificationWithout High-Level Controller Modification

E

F

t' t'

AA

B

a aB F

D EG

GH H

C

C

a'

a' b

b c

c

d

d

e

e

f

f g

g

D

tt' tt'

h h

30

Results (cont’)Results (cont’)

Desired Trajectory for the DFB task when an Unplanned Event Happened

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Results (cont’)Results (cont’)

Subject’s Actual Trajectories and Hand Configuration of the Subject for the Experiment

0 10 20 30 40 50 60 70 80 90 1000

0.1

0.2

0.3

Time (s)

Act

ual P

ositi

on

Y-d

irec

tion

(m)

0 10 20 30 40 50 60 70 80 90 1000

0.05

0.1

0.15

0.2

Time (s)

Act

ual P

ositi

on

Z-d

irec

tion

(m)

0 10 20 30 40 50 60 70 80 90 1000

0.5

1

1.5

Time (s)

Han

d

Con

figu

ratio

n

Hand Device is Active to Open Hand Hand Device is Active to Close Hand

a

a

b c

b c

d

d

e

e

gf

f g

h

h

t' tt'

h

Fully Hand Open

Hand Closing

Hand Opening

Fully HandClose

HandOpening

Fully Hand Open

HandOpening

Partially Hand Open (idle)

a b c

d e

f g

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Discussions and Discussions and ConclusionConclusion

A new intelligent control architecture is designed that can

supervise the arm and the hand assistive devices to produce necessary coordinated motion to complete a given ADL task,

monitor the progress and the safety of the ADL task such that necessary dynamic modifications of the task execution can be made (if needed) to complete the given task in a safe manner,

incorporate patient’s feedback in order to make the necessary modifications to impart effective therapy during the execution of the task in an automated manner

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Discussions and Discussions and Conclusion (cont’)Conclusion (cont’)

The proposed intelligent control architecture exploits hybrid system modeling techniques to accommodate both continuous and discrete systems in a unified manner

Hybrid system modeling technique is particularly useful in this context since it allows a systematic interface between the low-level assistive controllers and the high-level decision-making controller

The hybrid system modeling technique provides flexibility in interfacing low-level controllers without extensive redesign cost

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Discussions and Discussions and Conclusion (cont’)Conclusion (cont’)

Hybrid system modelling is mathematically rigorous and provides systematic design tools that are not limited by the number of states or events

New safety features as well as new task requirements can be incrementally added to the system by designing new events either by adding new sensors or by further analyzing the current sensory information and by adding new decision rules in the high-level controller

The patient’s feedback is easily integrated inside the proposed intelligent control architecture

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Contributions Contributions of of Hybrid Hybrid System Modelling TechniquesSystem Modelling Techniques

to Rehabilitation Roboticsto Rehabilitation Robotics The proposed hybrid system based control architecture provides a systematic procedure to effect changes such that the task execution could be automated

Instead of preprogramming numerous static trees based on if-then-else rules, it provides a dynamic mechanism of generating events that leads to necessary high-level decisions

A hybrid system based control mechanism could be useful in rehabilitation context in terms of coordinating decision making and assisting, monitoring safety, and managing and modifying code for automation

To our knowledge, such a hybrid system based control mechanism has not been explored in rehabilitation robotics

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Future WorkFuture Work Demonstrate the efficacy of the proposed intelligent control architecture with the stroke patients

Search for other hybrid system modelling techniques to develop a global control architecture

The efficient ways of human intention recognition systems are planned to be investigated that can recognize unclear words for stroke survivors who have aphasia

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Use of Hybrid System Modeling Technique fo r Robot- Assisted Rehabilitation Systems