Gaussian process gait trajectory learning and generation of collision-free motion for assist-as-needed rehabilitation

Jisoo Hong, Changmook Chun, Seung-Jong Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper introduces an approach to generate ground-collision-free gait motion by learning a statistical model of walking motion and applies assist-as-needed (AAN) training scheme in learned statistical model which is efficient for robotic gait rehabilitation. The method utilizes a nonlinear dimensionality reduction technique, which is based on Gaussian process, to construct the model using gait motion data obtained from several dozens of healthy subjects. The model is a common, averaged in statistical sense, low-dimensional representation of walking motion. Using the model, it is possible to generate a ground-collision-free gait trajectory at an arbitrary walking speed for a subject on the gait rehabilitation robot, and apply AAN training paradigm around the generated motion. We simulate the framework of learning and generation of motion with gait data from 50 healthy subjects, who walked on a motorized treadmill at 3 different speeds.

Original languageEnglish
Title of host publicationHumanoids 2015
Subtitle of host publicationHumanoids in the New Media Age - IEEE RAS International Conference on Humanoid Robots
PublisherIEEE Computer Society
Pages181-186
Number of pages6
ISBN (Electronic)9781479968855
DOIs
Publication statusPublished - 2015 Dec 22
Externally publishedYes
Event15th IEEE RAS International Conference on Humanoid Robots, Humanoids 2015 - Seoul, Korea, Republic of
Duration: 2015 Nov 32015 Nov 5

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
Volume2015-December
ISSN (Print)2164-0572
ISSN (Electronic)2164-0580

Other

Other15th IEEE RAS International Conference on Humanoid Robots, Humanoids 2015
CountryKorea, Republic of
CitySeoul
Period15/11/315/11/5

Keywords

  • Gaussian processes
  • Legged locomotion
  • Pelvis
  • Training
  • Trajectory
  • Yttrium

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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  • Cite this

    Hong, J., Chun, C., & Kim, S-J. (2015). Gaussian process gait trajectory learning and generation of collision-free motion for assist-as-needed rehabilitation. In Humanoids 2015: Humanoids in the New Media Age - IEEE RAS International Conference on Humanoid Robots (pp. 181-186). [7363549] (IEEE-RAS International Conference on Humanoid Robots; Vol. 2015-December). IEEE Computer Society. https://doi.org/10.1109/HUMANOIDS.2015.7363549