Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Human-robot cooperative movement training: Learning a novel sensory motor transformation during walking with robotic assistance-as-needed | Journal of NeuroEngineering and Rehabilitation BioMed Central Research Human-robot cooperative movement training Learning a novel sensory motor transformation during walking with robotic assistance-as-needed Jeremy L Emken1 Raul Benitez1 3 and David J Reinkensmeyer 1 2 Open Access Address biomedical Engineering Department University of California at Irvine Irvine CA USA 2Mechanical and Aerospace Engineering Department University of California at Irvine Irvine CA USA and 3Automatic Control Department Universitat Politècnica de Catalunya Barcelona SPAIN Email Jeremy L Emken - emken@ Raul Benitez - David J Reinkensmeyer - dreinken@ Corresponding author Published 28 March 2007 Received 20 April 2006 Journal of NeuroEngineering and Rehabilitation 2007 4 8 doi 86 1743-0003-4-8 Accepted 28 March 2007 This article is available from http content 4 1 8 2007 Emken et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background A prevailing paradigm of physical rehabilitation following neurologic injury is to assist-as-needed in completing desired movements. Several research groups are attempting to automate this principle with robotic movement training devices and patient cooperative algorithms that encourage voluntary participation. These attempts are currently not based on computational models of motor learning. Methods Here we assume that motor recovery from a neurologic injury can be modelled as a process of learning a novel sensory motor transformation which allows us to study a simplified experimental protocol amenable to mathematical description. Specifically we use a robotic force field paradigm to impose a virtual impairment on the left leg of .
