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Design of a 6-DOF Robotic Gait Training System With Closed-Chain Foot Initiated Kinematics Control

[+] Author Affiliations
Wei Liu

Auburn University, Auburn, AL

John Kovaleski

University of South Alabama, Mobile, AL

Marcus Hollis

Blue Bay Research Inc., Navarre, FL

Paper No. DSCC2015-9617, pp. V001T09A001; 5 pages
  • ASME 2015 Dynamic Systems and Control Conference
  • Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Control; Advances in Robotics; Advances in Wind Energy Systems; Aerospace Applications; Aerospace Power Optimization; Assistive Robotics; Automotive 2: Hybrid Electric Vehicles; Automotive 3: Internal Combustion Engines; Automotive Engine Control; Battery Management; Bio Engineering Applications; Biomed and Neural Systems; Connected Vehicles; Control of Robotic Systems
  • Columbus, Ohio, USA, October 28–30, 2015
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5724-3
  • Copyright © 2015 by ASME


Robotic assisted rehabilitation, taking advantage of neuroplasticity, has been shown to be helpful in regaining some degree of gait performance. Robot-applied movement along with voluntary efferent motor commands coordinated with the robot allows optimization of motion training. We present the design and characteristics of a novel foot-based 6-degree-of-freedom (DOF) robot-assisted gait training system where the limb trajectory mirrored the normal walking gait. The goal of this study was to compare robot-assisted gait to normal walking gait, where the limb moved independently without robotics. Motion analysis was used to record the three-dimensional kinematics of the right lower extremity. Walking motion data were determined and transferred to the robotic motion application software for inclusion in the robotic trials where the robot computer software was programmed to produce a gait pattern in the foot equivalent to the gait pattern recorded from the normal walking gait trial. Results demonstrated that ankle; knee and hip joint motions produced by the robot are consistent with the joint motions in walking gait. We believe that this control algorithm provides a rationale for use in future rehabilitation, targeting robot-assisted training in people with neuromuscular disabilities such as stroke.

Copyright © 2015 by ASME



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