1. Electrical & Electronics Engineering Department
Use of Hybrid System Modeling Technique for Robot-Assisted Rehabilitation Systems
Duygun Erol
Yeditepe University
Electrical & Electronics Engineering Department
Istanbul, TURKEY 1 1

2. Motivation
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 2 2

3. Motivation
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 3 3

4. Robot-Assisted Rehabilitation Systems
MIT Manus
ARM Guide
GENTLE/s
MIME
ADLER
Rutgers Master II-ND
Carnegie Mellon Hand Device
CyberGrasp
HWARD 4 4

5. Limitations
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 5 5

6. Rehabilitation Therapy Approaches
Scope of the Research
Movement
Assisted
Therapy
Task-Oriented
Robot-Assisted
Rehabilitation
Systems for
Hand Movement
Arm Movement
Rehabilitation Therapy Approaches
Robot-Assisted
Rehabilitation
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 6 6

7. Intelligent Control Architecture
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 7 7

8. Intelligent Control Architecture8 8

9. Intelligent Control Architecture9 9

10. Intelligent Control Architecture10 10

11. Intelligent Control Architecture11 11

12. Intelligent Control Architecture12 12

13. Intelligent Control Architecture13 13

14. PLANT Rehabilitation Robotic System14 14

15. PLANT (cont’) Rehabilitation Robotic System15 15

16. PLANT (cont’) Rehabilitation Robotic System16 16

17. PLANT (cont’) Rehabilitation Robotic System17 17

18. PLANT (cont’) Human Intention Recognition18 18

19. Task Design (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 19 19

20. Design Details of the Intelligent Controller (Theory)
The discrete event system (DES) plant model (plant and interface) is a nondeterministic finite automaton
The DES controller (high-level controller) is modeled as a discrete-event system (DES) deterministic finite automaton 20 20

21. Design Details of the 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: 21 21

22. Design Details of the Intelligent Controller (cont’)
The plant symbols in
are found using: 22 22

23. Design Details of the Intelligent Controller (cont’)
The control states in
are found using: 23 23

24. Design Details of the Intelligent Controller (cont’)
The control symbols in
are found using: 24 24

25. Design Details of the Intelligent Controller (cont’)25 25

26. Experiments
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 26 26

27. Results (Validation of Human Intention Recognition System)27 27

28. Results (Evaluation of the Proposed Intelligent Control Architecture )
Desired Motion Trajectory 28 28

29. Control Mechanism 29 29

30. Desired Trajectory for the DFB task when an Unplanned Event Happened
Results (cont’)
Desired Trajectory for the DFB task when an Unplanned Event Happened 30 30

31. Results (cont’)
Subject’s Actual Trajectories and Hand Configuration of
the Subject for the Experiment 31 31

32. 32 32

33. Discussions and Conclusion
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 33 33

34. Discussions and 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 34 34

35. Discussions and 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
Hybrid system modeling is mathematically rigorous and provides systematic design tools that are not limited by the number of states or events. This aspect of hybrid system design is particularly important since more complex ADL tasks will require design and analysis of larger number of states and events. 35 35

36. Contributions of Hybrid System Modelling Techniques to 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 36 36

37. Future 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 37 37

38. THANK YOU 38 38