Transcript Document

An Active Orthosis For
Cerebral Palsy Children
A. Calanca, S. Piazza, P. Fiorini, A.Cosentino
ALTAIR Robotics Laboratory
Computer Science Department
University of Verona
21/07/2015
A.Calanca
A.Calanca
Sept 24, 2012
1
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
The ARGO Prototype
Actuation:
• Pneumatic Muscle
• Force Control
• Reciprocation
Sensors:
• Muscle force
• Hip and knee angles
• Ground reaction forces
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The ARGO Prototype
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The ARGO Prototype
Interaction with the test patient
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Results
Patient condition before active orthosis
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Results
Autonomous walking: the user is able to keep a fluid walking and
also start it without external help.
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Results
Rehabilitation: our test patients show a gradual improvement of his
motion capability.
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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.
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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!
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Actuator modelling
Chou and Hannaford model is not suitable for Festo muscles, due
to different mechanical structure.
Validation result (LS identification - linear parameterisation):
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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
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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
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Sept 24, 2012
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).
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Control System
Comparison with low
gain PID: the
response is stable
and not noisy and but
following errors are
very big
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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!
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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
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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).
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Torque Computation
Then accordingly to
FSM state, we calculate
the desired torque
basing on gravity
compensation and the
equilibrium of the
patient.
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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.
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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.
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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
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Sept 24, 2012
Thanks
Results
The active orthosis does not produce a
significative decrease in user fatigue and
in muscle recruitment
• Metabolic cost analysis
• EMG analysis
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.
Result
Autonomous
Start
(3 trials)
Continuous
Walking Duration
NF-Walker
(before ARGO)
0/3
3-5 s
(with little help)
ARGO
3/3
>200 s
NF-Walker
(after ARGO)
3/3
>200 s
Background: Active orthoses
Self Balanced
RewalkTM,
AAFO, WWH,
HAL
Mobile
Not mobile
Not Self
Balanced
Lokomat®,
GangtrainerTM,
Innowalk®,
SUBAR
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.