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Published January 1, 2008 | public
Journal Article Open

Feasibility of Manual Teach-and-Replay and Continuous Impedance Shaping for Robotic Locomotor Training Following Spinal Cord Injury

Abstract

Robotic gait training is an emerging technique for retraining walking ability following spinal cord injury (SCI). A key challenge in this training is determining an appropriate stepping trajectory and level of assistance for each patient, since patients have a wide range of sizes and impairment levels. Here, we demonstrate how a lightweight yet powerful robot can record subject-specific, trainer-induced leg trajectories during manually assisted stepping, then immediately replay those trajectories. Replay of the subject-specific trajectories reduced the effort required by the trainer during manual assistance, yet still generated similar patterns of muscle activation for six subjects with a chronic SCI. We also demonstrate how the impedance of the robot can be adjusted on a step-by-step basis with an error-based, learning law. This impedance-shaping algorithm adapted the robot's impedance so that the robot assisted only in the regions of the step trajectory where the subject consistently exhibited errors. The result was that the subjects stepped with greater variability, while still maintaining a physiologic gait pattern. These results are further steps toward tailoring robotic gait training to the needs of individual patients.

Additional Information

© Copyright 2008 IEEE. Reprinted with permission. Manuscript received September 27, 2006; revised April 7, 2007. [Posted online: 2007-12-17] This work was supported by ATP 00-00-4906 and the NIDRR RERC on Spinal Cord Injury under Grant H133E020732. The work of J.L. Emken was supported by an Achievement Reward for College Scientists Scholarship. The authors would like to thank A. Budovitch and R. van den Brand of the UCLA Human Locomotion Research Center for their assistance in planning and running the experiments, to D. Aoyagi for his assistance in running the experiments, to C. Angeli for her assistance in data analysis, and to R. Edgerton for suggestions on the study design and reviewing the manuscript.

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August 22, 2023
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