Robotic technology to aid and assess recovery and learning in stroke patients

Each year, about 700,000 people in the United States have a stroke, making it a leading cause of serious, long-term disability. Modalities of therapy often assume the processes underlying motor recovery and motor learning are similar because both exhibit activity- dependent neural plasticity. However, the impact of other factors unique to recovery such as re-acquisition of muscle strength and resolution of abnormal muscle tone confounds the validity of this assumption. By implementing an adaptive impedance controller that collapses from a "virtual slot" between two targets to a "virtual spring" at the desired target, a new performance-based progressive therapy (PBPT) algorithm was developed to test whether recovery would be enhanced by incorporating learning strategies like repetition, goal specification, and positive reinforcement. A study of chronic stroke patients (8 to 95 months post-stroke) who were in a clinically verified "stable" phase of recovery was conducted with the PBPT protocol, in which patients made over 12,000 visually guided, point-to-point movements.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Mechanical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2005

Author dc:creator

• Palazzolo, Jerome J

Advisor dc:contributor.advisor

• Neville Hogan.

• Mechanical Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/33918

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/33918