An adaptable human-like gait pattern generator derived from a lower limb exoskeleton

Rafael Mendoza-Crespo, Diego Torricelli, Joel Carlos Huegel*, Jose Luis Gordillo, Jose L Pons, Rogelio Soto

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Walking rehabilitation processes include many repetitions of the same physical movements in order to replicate, as close as possible, the normal gait trajectories, and kinematics of all leg joints. In these conventional therapies, the therapist's ability to discover patient's limitations-and gradually reduce them-is key to the success of the therapy. Lower-limb robotic exoskeletons have strong deficiencies in this respect as compared to an experienced therapist. Most of the currently available robotic solutions are not able to properly adapt their trajectories to the biomechanical limitations of the patient. With this in mind, much research and development is still required in order to improve artificial human-like walking patterns sufficiently for valuable clinical use. The work herein reported develops and presents a method to acquire and saliently analyze subject-specific gait data while the subject dons a passive lower-limb exoskeleton. Furthermore, the method can generate adjustable, yet subject-specific, kinematic gait trajectories useful in programming controllers for future robotic rehabilitation protocols. A human-user study with ten healthy subjects provides the experimental setup to validate the proposed method. The experimental protocol consists in capturing kinematic data while subjects walk, with the donned H2 lower-limb exoskeleton, across three experimental conditions: walking with three different pre-determined step lengths marked on a lane. The captured ankle trajectories in the sagittal plane were found by normalizing all trials of each test from one heel strike to the next heel strike independent of the specific gait features of each individual. Prior literature suggests analyzing gait in phases. A preliminary data analysis, however, suggests that there exist six key events of the gait cycle, events that can adequately characterize gait for the purposes required of robotic rehabilitation including gait analysis and reference trajectory generation. Defining the ankle as an end effector and the hip as the origin of the coordinate frame and basing the linear regression calculations only on the six key events, i.e., Heel Strike, Toe Off, Pre-Swing, Initial Swing, Mid-Swing, and Terminal Swing, it is possible to generate a new calculated ankle trajectory with an arbitrary step length. The Leave-One-Out Cross Validation algorithm was used to estimate the fitting error of the calculated trajectory vs. the characteristic captured trajectory per subject, showing a fidelity average value of 95.2, 96.1, and 97.2%, respectively, for each step-length trial including all subjects. This research presents method to capture ankle trajectories from subjects and generate human-like ankle trajectories that could be scaled and computed on-line, could be adjusted to different gait scenarios, and could be used not only to generate reference trajectories for gait controllers, but also as an accurate and salient benchmark to test the human likeness of gait trajectories employed by existing robotic exoskeletal devices.

Original languageEnglish (US)
Article number36
JournalFrontiers Robotics AI
Volume6
Issue numberMAY
DOIs
StatePublished - Jan 1 2019

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Trajectories
Robotics
Patient rehabilitation
Kinematics
Exoskeleton (Robotics)
Gait analysis
Controllers
End effectors
Linear regression

Keywords

  • Ankle
  • Eigenvalue decomposition
  • Gait
  • Heel strike
  • Key events
  • Step length
  • Toe off
  • Trajectory

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence

Cite this

Mendoza-Crespo, Rafael ; Torricelli, Diego ; Huegel, Joel Carlos ; Gordillo, Jose Luis ; Pons, Jose L ; Soto, Rogelio. / An adaptable human-like gait pattern generator derived from a lower limb exoskeleton. In: Frontiers Robotics AI. 2019 ; Vol. 6, No. MAY.
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An adaptable human-like gait pattern generator derived from a lower limb exoskeleton. / Mendoza-Crespo, Rafael; Torricelli, Diego; Huegel, Joel Carlos; Gordillo, Jose Luis; Pons, Jose L; Soto, Rogelio.

In: Frontiers Robotics AI, Vol. 6, No. MAY, 36, 01.01.2019.

Research output: Contribution to journalArticle

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T1 - An adaptable human-like gait pattern generator derived from a lower limb exoskeleton

AU - Mendoza-Crespo, Rafael

AU - Torricelli, Diego

AU - Huegel, Joel Carlos

AU - Gordillo, Jose Luis

AU - Pons, Jose L

AU - Soto, Rogelio

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KW - Ankle

KW - Eigenvalue decomposition

KW - Gait

KW - Heel strike

KW - Key events

KW - Step length

KW - Toe off

KW - Trajectory

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