Combining FES with robot therapy
in upper limb stroke rehabilitation
Responding to personal performance
using Iterative Learning Control (ILC)
Jane Burridge
University of Southampton,
UK
[email protected]
@janeburridge2
Rehabilitation Robots
•  Enables the patient to achieve a task
– Repetitive goal orientated practice requiring attention
– Tasks can be adjusted to provide success at the limit of
performance
– Motivating and varied – VR / games – less boring than
PT!
•  Evidence for improved motor control [Kwakkel 2008,
Prange 2006].
The trouble with Functional
Electrical Stimulation!
•  The concept….
•  Evidence for improved function and
reduced impairment [EBRSR 2012] - may be greater
in acute stroke [Wang 2002]
•  Enhanced when voluntary activated [de Kroon 2005]
–  Hebbian learning/Associated stimuli (Rushton
2001)
–  Open loop control - buttons, EMG, inertial sensors
•  Incentive for the patient to use their voluntary effort?
Can we provide performance controlled
FES by using a robot?
4
Iterative learning control
– how it works:
•  Patient attempts a task
•  Define a trajectory to follow and measure the
difference between what the patient achieves and the
trajectory
•  Repeat the movement adjusting the stimulation so
that:
•  New stimulation profile = previous profile plus a
correction term
5
•  If the patient improves with
each repetition the stimulation
component decreases and
voluntary contribution increases
•  If the patient becomes fatigued then stimulation increases
to compensate for decrease (or inappropriate) voluntary
effort
•  Different tasks (trajectories) can be used in sequence to
maintain attention
6
Three studies
•  Stroke patients with moderate to severe upper limb
function
•  18 x 60 min sessions over 8 weeks
•  Assessed by ARAT and FM
•  Qualitative feedback
•  Study 1. used a single channel of FES (triceps) and a planar
robot – only elbow and shoulder extension
7
Learning Control (ILC) using a Robot & FES - ILC algorithm
applies during extension phase only (stage 1)
8
Study 1 study: Planar Stroke Rehabilitation – Results
• 
Convergence is typically achieved
in 5 iterations to an RMS error of
less than 10mm.
• 
Preliminary results with the first 4
stroke patients for:
•  a) 20s trajectories
•  b) 10s trajectories
Tracking results
Fig 2
Fig 1
• 
Fig 1 shows the UNSTIMULTED error at
each session for each subject
• 
Fig 2a shows the mean corrected error in
one task at each session for all subjects
• 
Fig 2b shows the % max stimulation used
Hughes Burridge, et al
Upper limb rehabilitation post stroke using Iterative Learning Control
mediated by Functional Electrical Stimulation. JNNR 2009;
10
Study 2 using the Armeo: 3D tasks using 2-channel
stimulation (anterior deltoid and triceps)
Results: ILC was effective in improving tracking performance
and stimulation output was reduced (data from one subject)
Black – trajectory
Blue – 1st trial
Red – 6th trial
Red - Stimulation output 1st trial
Blue – Stimulation output 6th trial
Clinical Results
P. Id. 01 02 03 04 05 Mean
(SD) z-test:
ARAT (57 ) Baseline Post- 0 1 7 10 9 10 4 0 12 13 6.4
6.8
(4.62) (5.89) F-M (Motor – 66) Baseline Post- 9.5 20 19 33 31 44 16 21 42 46 23.5
32.8
(12.95) (12.28) z(5) = -.69, p = .49
z(5) = -2.02, p = .04
No improvement in func/on Change 16% 21% 20% 8% 6% 14% Conclusions
•  Feasibility was demonstrated
•  System was well accepted by patients
•  Reduction in impairment
•  No improvement in function requiring dexterity
•  The robot is not practical for use at home
Stage 3:
•  Including shoulder, wrist and hand movement
•  Low functioning patients used the SaeboMAS
•  Performing free, unconstrained, goal orientated functional
tasks
•  No Target to track!
16
Multichannel electrode
arrays embedded in a
sleeve
SaeboMas support for
lower functioning
patients
17
Methods
•  Cameras and wearable sensors monitored position with reference to a
biomechanical model
•  Five tasks based on the MAL: closing a drawer, switching on a light
switch, stabilising an object, button pressing and repositioning an
object
•  ILC algorithms based on normative data for each task
•  Stimulation: anterior deltoid, triceps and wrist and finger extensors Max levels for comfort set at initial session
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Turning on a light switch (SaeboMAS only)
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Turning on a light switch with ILC - FES
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Clinical Results
ID 01 ARAT (57 ) Baseline Post- 0 7 F-M Motor (66) Baseline Post- 15 24 02 3 7 19 24 03 4 5 17 21 04 3 8 21 27 05 3 8 22 20 Mean
(SD) 2.6 (1.52) z-test:
t(4) = -‐2.44, p = .036 Improved func/on 7 (1.22) 18.8 (2.86) 23.2 (2.77) t(4) = -‐4.49, p = .005 Reduced impairment Summary
•  FES & Robot therapy enabled low functioning patients to practice
•  Training of elbow and shoulder movements (first 2 studies) reduced
impairment (improved FM score)
•  Training on functional tasks involving dexterity (study 3) may improve
function (ARAT)
•  The training programs were feasible and tolerated by the patients – all
patients completed all studies with no drop-outs and gave postive
feedback
•  Questions remain:
–  Needs to be tested in a larger trial including long-term benefits
–  How made feasible clinical practice – or at home?
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Acknowledgements
•  Colleagues in the Rehabilitation and Health Technology Research
Group at the University of Southampton, Engineers and Psychologists
•  The ILC research team: Chris Freeman (control Engineer), Ann-Marie
Hughes; Tim Exell (Biomechanics), Katie Meadmore(Psychologist) Eric
Rogers, Emma Hallewell (Rehabilitation PhD student)
•  Funding and support from UK NIHR, EPSRC and Support from
Hocoma