http://nnr.sagepub.com/content/26/7/822.abstract?etoc
Abstract
Background. Robot-aided
neurorehabilitation can provide intensive, repetitious training to
improve upper-limb function after stroke.
To be more effective, motor therapy ought to be
progressive and continuously challenge the patient’s ability. Current
robotic
systems have limited customization capability and
require a physiotherapist to assess progress and adapt therapy
accordingly.
Objective. The authors aimed to track motor improvement during robot-assistive training and test a tool to more automatically adjust
training. Methods. A total of 18
participants with chronic stroke were trained using a multicomponent
reaching task assisted by a shoulder–elbow
robotic assist. The time course of motor gains was
assessed for each subtask of the practiced exercise. A statistical
algorithm
was then tested on simulated data to validate its
ability to track improvement and subsequently applied to the recorded
data
to determine its performance compared with a
therapist. Results. Patients’ recovery of motor function
exhibited a time course dependent on the particular component of the
executed task,
suggesting that differential training on a subtask
level is needed to continuously challenge the neuromuscular system and
boost recovery. The proposed algorithm was tested
on simulated data and was proven to track overall patient’s progress
during
rehabilitation. Conclusions. Tuning of the training program at the subtask level may accelerate the process of motor relearning. The algorithm proposed
to adjust task difficulty opens new possibilities to automatically customize robotic-assistive training.
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