http://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-016-0189-7#Abs1
- Patricia LeconteEmail author and
- Renaud Ronsse
Journal of NeuroEngineering and Rehabilitation201613:82
DOI: 10.1186/s12984-016-0189-7
© The Author(s) 2016
Received: 21 January 2016
Accepted: 26 August 2016
Published: 13 September 2016
Abstract
Background
Rhythmic and discrete upper-limb
movements are two fundamental motor primitives controlled by different
neural pathways, at least partially. After stroke, both primitives can
be impaired. Both conventional and robot-assisted therapies mainly train
discrete functional movements like reaching and grasping. However, if
the movements form two distinct neural and functional primitives, both
should be trained to recover the complete motor repertoire. Recent
studies show that rhythmic movements tend to be less impaired than
discrete ones, so combining both movement types in therapy could support
the execution of movements with a higher degree of impairment by
movements that are performed more stably.
Methods
A new performance-based assistance
method was developed to train rhythmic movements with a rehabilitation
robot. The algorithm uses the assist-as-needed paradigm by independently
assessing and assisting movement features of smoothness, velocity, and
amplitude. The method relies on different building blocks: (i) an
adaptive oscillator captures the main movement harmonic in state
variables, (ii) custom metrics measure the movement performance
regarding the three features, and (iii) adaptive forces assist the
patient. The patient is encouraged to improve performance regarding
these three features with assistance forces computed in parallel to each
other. The method was tested with simulated jerky signals and a pilot
experiment with two stroke patients, who were instructed to make
circular movements with an end-effector robot with assistance during
half of the trials.
Results
Simulation data reveal sensitivity of
the metrics for assessing the features while limiting interference
between them. The assistance’s effectiveness with stroke patients is
established since it (i) adapts to the patient’s real-time performance,
(ii) improves patient motor performance, and (iii) does not lead the
patient to slack. The smoothness assistance was by far the most used by
both patients, while it provided no active mechanical work to the
patient on average.
Conclusion
Our performance-based assistance method
for training rhythmic movements is a viable candidate to complement
robot-assisted upper-limb therapies for training a larger motor
repertoire.
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