A.Calanca Sept 24, 2012

1. An ActiveOrthosis For Cerebral Palsy Children A. Calanca, S. Piazza, P. Fiorini, A.Cosentino ALTAIR Robotics Laboratory Computer Science Department University of Verona A.Calanca A.Calanca Sept 24, 2012

2. Background: Cerebral Palsy Cerebral palsy has an incidence of birth between 0.15 and 0.25%. Precocity of rehabilitation has a fundamental role in prevention of secondary deformity and anomalous motor development [Viurtello1984]. Also recent studies pay great attention on physical therapy applied to young CP patients, focusing on movement based strategy and physical training [Dodd 2002][Damiano 2006]. This kind of treatments are quite expensive because they need the presence of one or more physiotherapists. Orthotic systems try to help this treatment relieving physiotherapist of part of work. A.Calanca Sept 24, 2012

3. The ARGO Prototype • Actuation: • Pneumatic Muscle • Force Control • Reciprocation • Sensors: • Muscle force • Hip and knee angles • Ground reaction forces A.Calanca Sept 24, 2012

4. The ARGO Prototype A.Calanca Sept 24, 2012

5. The ARGO Prototype Interaction with the test patient A.Calanca Sept 24, 2012

6. Results Patient condition before active orthosis A.Calanca Sept 24, 2012

7. Results Autonomous walking: the user is able to keep a fluid walking and also start it without external help. A.Calanca Sept 24, 2012

8. Results Rehabilitation: our test patients show a gradual improvement of his motion capability. A.Calanca Sept 24, 2012

9. Actuation System • McKibben Pneumatic Artificial Muscles • Intrinsic safety and compliance • High power to weight ratio • High forces • Low cost • Supply via small high pressure air bottle. In particular we use Festo manufactured muscles: they have an unique layer of mixed rubber and fibers that improves muscle safety and long lasting in respect with classical McKibben. Disadvantages: non-linear behaviour, control issue. A.Calanca Sept 24, 2012

10. Actuator modelling Chou and Hannaford model for classic McKibben muscles: F is the force, P is the pressure and θ is the fiber angle. We can put the same model in a different form involving muscle length (L) instead of θ, which is difficult to measure This is more convenient for identification! A.Calanca Sept 24, 2012

11. Actuator modelling Chou and Hannaford model is not suitable for Festo muscles, due to different mechanical structure. Validation result (LS identification - linear parameterisation): A.Calanca Sept 24, 2012

12. Control System Neural Network Type: feed-forward, back propagation Topology: 8 neurones (2 input, 5 middle, 1 output) Training Data collected from test bed experiments at different pressure levels and different force frequencies. Pressure and muscle length as input, force as target A.Calanca Sept 24, 2012

13. Control System We use a neural network (NN) feed-forward action for ensure controller fast response and a low gain PID for stabilisation. The NN calculates the required pressure knowing the Force reference and the muscle length Note: The muscle model M is coupled with the mechanical systems dynamics (DYN) through muscle length A.Calanca Sept 24, 2012

14. Control System Square and sine wave response of the proposed controller. Maximum overshoot of step response is 0.87N while maximum sine following error is 1.31N. Average errors are 0.15 (square) and 0.37N (sine). A.Calanca Sept 24, 2012

15. Control System Comparison with low gain PID: the response is stable and not noisy and but following errors are very big A.Calanca Sept 24, 2012

16. Control System Comparison with high gain PID: the response is fast but is too noisy. Note: There is no usable compromise between the showed low gain and high gain configurations! A.Calanca Sept 24, 2012

17. Control System Response of the proposed controller in orthosis usage. The maximum error is of 1.31N. Note: The human leg has more filtering action with respect to the test bed. Some oscillation still occurs but they are independent from set point dynamics as we expect A.Calanca Sept 24, 2012

18. Torque Computation It generates the hip torque profiles basing on sensor input and system knowledge. It uses a simple algorithm for gait phase recognition, based on a finite state machine (FSM). A.Calanca Sept 24, 2012

19. Torque Computation Then accordingly to FSM state, we calculate the desired torque basing on gravity compensation and the equilibrium of the patient. A.Calanca Sept 24, 2012

20. Results Torque, position and FSM state data from session with test patient. The system is not cohercitive and is able to understand user intention. Plot shows FSM states in a double left step. A.Calanca Sept 24, 2012

21. Results Patient condition before active orthosis Two years of treatment with passive orthosis (same mechanical structure). Patient is not autonomous in walking and needs help from physiotherapist. A.Calanca Sept 24, 2012

22. Conclusions Experiments with a cerebral palsy patient show very encouraging results. He was not only able to walk autonomously but also to improve his capability in passive orthosis usage. This can be due to the interaction with the orthosis that doesn’t make the user passive but follows his action plan. Patient action plan Vs Physiotherapist action plan A.Calanca Sept 24, 2012

23. Thanks

24. Results • The active orthosis does not produce a significative decrease in user fatigue and in muscle recruitment • Metabolic cost analysis • EMG analysis

25. Future Works We need to carry on experiments with more CP patient for having more scientific evidence of benefits We propose to investigate if there is an improvement in patient condition. How much is the improvement? In wich aspects? Is it dependent from patient initial condition? How? What can be the best way to help patient? Are all still open question.

26. Result

27. Background: Active orthoses Table shows that there is not significant active orthosis which is mobile and can keep user balance. Note: Only most famous rehabilitative/assistive devices are included in table.