Article

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.

Langue : ANGLAIS