By Topic

Taking a Lesson From Patients' Recovery Strategies to Optimize Training During Robot-Aided Rehabilitation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Colombo, R. ; IRCCS, “Salvatore Maugeri” Found., Pavia, Italy ; Sterpi, I. ; Mazzone, A. ; Delconte, C.
more authors

In robot-assisted neurorehabilitation, matching the task difficulty level to the patient's needs and abilities, both initially and as the relearning process progresses, can enhance the effectiveness of training and improve patients' motivation and outcome. This study presents a Progressive Task Regulation algorithm implemented in a robot for upper limb rehabilitation. It evaluates the patient's performance during training through the computation of robot-measured parameters, and automatically changes the features of the reaching movements, adapting the difficulty level of the motor task to the patient's abilities. In particular, it can select different types of assistance (time-triggered, activity-triggered, and negative assistance) and implement varied therapy practice to promote generalization processes. The algorithm was tuned by assessing the performance data obtained in 22 chronic stroke patients who underwent robotic rehabilitation, in which the difficulty level of the task was manually adjusted by the therapist. Thus, we could verify the patient's recovery strategies and implement task transition rules to match both the patient's and therapist's behavior. In addition, the algorithm was tested in a sample of five chronic stroke patients. The findings show good agreement with the therapist decisions so indicating that it could be useful for the implementation of training protocols allowing individualized and gradual treatment of upper limb disabilities in patients after stroke. The application of this algorithm during robot-assisted therapy should allow an easier management of the different motor tasks administered during training, thereby facilitating the therapist's activity in the treatment of different pathologic conditions of the neuromuscular system.

Published in:

Neural Systems and Rehabilitation Engineering, IEEE Transactions on  (Volume:20 ,  Issue: 3 )