Robotic assistance that encourages the generation of stepping rather than fully assisting movements is best for learning to step in spinally contused rats.

Autor: Lee C; Department of Biological Sciences, California State University, Los Angeles, CA 90032-8162, USA., Won D, Cantoria MJ, Hamlin M, de Leon RD
Jazyk: angličtina
Zdroj: Journal of neurophysiology [J Neurophysiol] 2011 Jun; Vol. 105 (6), pp. 2764-71. Date of Electronic Publication: 2011 Mar 23.
DOI: 10.1152/jn.01129.2010
Abstrakt: 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.
Databáze: MEDLINE