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Báo cáo hóa học: " Robotic neurorehabilitation: a computational motor learning perspective"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Robotic neurorehabilitation: a computational motor learning perspective | Journal of NeuroEngineering and Rehabilitation BioMed Central Open Access Review Robotic neurorehabilitation a computational motor learning perspective Vincent S Huang and John W Krakauer Address Motor Performance Laboratory Department of Neurology The Neurological Institute Columbia University College of Physicians and Surgeons New York New York USA Email Vincent S Huang -vh2181@columbia.edu JohnWKrakauer-jwk18@columbia.edu Corresponding author Published 25 February 2009 Received 13 January 2009 Journal of NeuroEngineering and Rehabilitation 2009 6 5 doi l0.ll 86 1743-0003-6-5 Accepted 25 February 2009 This article is available from http www.jneuroengrehab.cOm content 6 1 5 2009 Huang and Krakauer licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http creativecommons.org licenses by 2.0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Conventional neurorehabilitation appears to have little impact on impairment over and above that of spontaneous biological recovery. Robotic neurorehabilitation has the potential for a greater impact on impairment due to easy deployment its applicability across of a wide range of motor impairment its high measurement reliability and the capacity to deliver high dosage and high intensity training protocols. We first describe current knowledge of the natural history of arm recovery after stroke and of outcome prediction in individual patients. Rehabilitation strategies and outcome measures for impairment versus function are compared. The topics of dosage intensity and time of rehabilitation are then discussed. Robots are particularly suitable for both rigorous testing and application of motor learning principles to neurorehabilitation. Computational motor control and learning principles derived from studies in healthy subjects are introduced in the context of robotic .