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Speed-dependent reference joint trajectory generation for robotic gait support.

B Koopman1, E H F van Asseldonk1, H van der Kooij2

  • 1Department of Biomechanical Engineering, University of Twente, Enschede 7500 AE, The Netherlands.

Journal of Biomechanics
|February 18, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to create personalized gait trajectories for robotic rehabilitation. The models accurately predict joint movements based on walking speed and body height, improving control for assistive devices.

Keywords:
KinematicsPrediction methodsReference joint trajectoriesRegression analysisRobotic gait supportWalking-speed

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Area of Science:

  • Biomechanics
  • Robotics
  • Rehabilitation Engineering

Background:

  • Actuated orthoses and gait rehabilitation robotics require accurate kinematic reference trajectories.
  • These trajectories are significantly influenced by walking speed.

Purpose of the Study:

  • To develop and validate a novel method for reconstructing body-height and speed-dependent joint trajectories.
  • To provide adaptable gait patterns for robotic applications and pathological gait assessment.

Main Methods:

  • Collected gait kinematics from 15 healthy adults across various walking speeds (0.5-5 kph).
  • Developed regression models to predict key joint trajectory events (timing, angle, velocity, acceleration) based on walking speed and body height.
  • Utilized quintic splines to reconstruct complete gait cycles from predicted key events.

Main Results:

  • Walking speed was a primary determinant of gait key-event timing and amplitude; body height had a lesser effect.
  • Reconstructed trajectories closely matched measured data for angles, angular velocities, and accelerations.
  • Achieved a Root Mean Square Error (RMSE) of 2.6° for angles and a mean correlation coefficient of 0.91 for angular trajectories.

Conclusions:

  • The developed method effectively generates accurate, speed-dependent kinematic gait patterns.
  • These patterns are suitable for controlling robotic gait applications and serving as a reference for assessing pathological gait.