Technical Brief

Design of a Single Degree-of-Freedom, Adaptable Electromechanical Gait Trainer for People With Neurological Injury

[+] Author and Article Information
Sung Yul Shin

Department of Mechanical Engineering,
University of Texas at Austin,
204 E Dean Keeton St,
Austin, TX 78712
e-mail: syshin0228@utexas.edu

Ashish D. Deshpande

Department of Mechanical Engineering,
University of Texas at Austin,
204 E Dean Keeton St,
Austin, TX 78712
e-mail: ashish@austin.utexas.edu

James Sulzer

Department of Mechanical Engineering,
University of Texas at Austin,
204 E Dean Keeton St,
Austin, TX 78712
e-mail: james.sulzer@austin.utexas.edu

1Corresponding author.

Contributed by the Mechanisms and Robotics Committee of ASME for publication in the JOURNAL OF MECHANISMS AND ROBOTICS. Manuscript received August 17, 2017; final manuscript received March 21, 2018; published online May 31, 2018. Assoc. Editor: Veronica J. Santos.

J. Mechanisms Robotics 10(4), 044503 (May 31, 2018) (7 pages) Paper No: JMR-17-1258; doi: 10.1115/1.4039973 History: Received August 17, 2017; Revised March 21, 2018

The cost of therapy is one of the most significant barriers to recovery after neurological injury. Robotic gait trainers move the legs through repetitive, natural motions imitating gait. Recent meta-analyses conclude that such training improves walking function in neurologically impaired individuals. While robotic gait trainers promise to reduce the physical burden on therapists and allow greater patient throughput, they are prohibitively costly. Our novel approach is to design a new single degree-of-freedom (DoF) robotic trainer that maintains the key advantages of the expensive trainers but with a simplified design to reduce cost. Our primary design challenge is translating the motion of a single actuator to an array of natural gait trajectories. We address this with an eight-link Jansen mechanism that matches a generalized gait trajectory. We then optimize the mechanism to match different trajectories through link length adjustment based on nine different gait patterns obtained from gait database of 113 healthy individuals. To physically validate the range in gait patterns produced by the simulation, we tested kinematic accuracy on a motorized wooden proof-of-concept of the gait trainer. The simulation and experimental results suggested that an adjustment of two links can reasonably fit a wide range of gait patterns under typical within-subject variance. We conclude that this design could provide the basis for a low-cost, patient-based electromechanical gait trainer for neurorecovery.

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Grahic Jump Location
Fig. 1

Rendering of robotic gait trainer utilizing a single motor to produce a natural gait trajectory. The device can produce adaptable gait patterns by link adjustments.

Grahic Jump Location
Fig. 2

Mean of total 113 healthy subjects' gait pattern (meta-trajectory) and total nine gait patterns with different step lengths in x–y plane (average of ten subjects for large, medium, and small step lengths and their intermediates)

Grahic Jump Location
Fig. 3

Parameterized structure of the proposed mechanism. The end-effector path at PE is determined based on the angle of the rotary crank, θ2 and the configuration of 12 links with given lengths.

Grahic Jump Location
Fig. 4

Optimized to the meta-trajectory, the structure results in an RMS error of 3.09 cm: (a) crosses delineate the meta-trajectory, and circles show the predicted trajectory and (b) meta-trajectory and predicted endpoint trajectories in gait cycle domain with a constant crank (L1) input angular velocity

Grahic Jump Location
Fig. 5

Gait pattern error for number of adjustable links. Selected adjustable links are denoted on each bar graph. Adjusting just two links (L4 and L8) provides the largest drop in error for the fewest adjustments.

Grahic Jump Location
Fig. 6

The RMS error for each canonical gait pattern given two link adjustments shows consistent performance across patterns, with expectedly larger error in larger gait patterns

Grahic Jump Location
Fig. 7

(Left) Motorized wooden model of the gait trainer with accompanying range of motion reflecting a natural gait pattern. (Right) Experimental results using optical motion capture system by adjusting link lengths to match with three different step lengths (largest-top, medium-middle and smallest-bottom).

Grahic Jump Location
Fig. 8

Rendering of expected alpha prototype. The design of the gait trainer involves a robust frame with body weight support, a removable pelvic support, a treadmill, and the operational linkage mechanism, driven by a DC gear motor and belt drive.



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