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Robotic assistance that encourages the generation of stepping rather than fully assisting movements is best for learning to step in spinally contused rats
Author(s) -
Connie Lee,
Deborah Won,
Mary Jo Cantoria,
Marvin Hamlin,
Ray D. de Leon
Publication year - 2011
Publication title -
journal of neurophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.302
H-Index - 245
eISSN - 1522-1598
pISSN - 0022-3077
DOI - 10.1152/jn.01129.2010
Subject(s) - ankle , physical medicine and rehabilitation , treadmill , spinal cord injury , kinematics , electromyography , psychology , motor learning , rehabilitation , gait training , medicine , computer science , physical therapy , spinal cord , neuroscience , anatomy , physics , classical mechanics
Robotic devices have been developed to assist body weight-supported treadmill training (BWSTT) in individuals with spinal cord injuries (SCIs) and stroke. Recent findings have raised questions about the effectiveness of robotic training that fully assisted (FA) stepping movements. The purpose of this study was to examine whether assist-as-needed robotic (AAN) training was better than FA movements in rats with incomplete SCI. Electromyography (EMG) electrodes were implanted in the tibialis anterior and medial gastrocnemius hindlimb muscles of 14 adult rats. Afterward, the rats received a severe midthoracic spinal cord contusion and began daily weight-supported treadmill training 1 wk later using a rodent robotic system. During training, assistive forces were applied to the ankle when it strayed from a desired stepping trajectory. The amount of force was proportional to the magnitude of the movement error, and this was multiplied by either a high or low scale factor to implement the FA (n = 7) or AAN algorithms (n = 7), respectively. Thus FA training drove the ankle along the desired trajectory, whereas greater variety in ankle movements occurred during AAN training. After 4 wk of training, locomotor recovery was greater in the AAN group, as demonstrated by the ability to generate steps without assistance, more normal-like kinematic characteristics, and greater EMG activity. The findings suggested that flexible robotic assistance facilitated learning to step after a SCI. These findings support the rationale for the use of AAN robotic training algorithms in human robotic-assisted BWSTT.

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