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.
|Title of host publication
|Subtitle of host publication
|Humanoids in the New Media Age - IEEE RAS International Conference on Humanoid Robots
|IEEE Computer Society
|Number of pages
|Published - 2015 Dec 22
|15th IEEE RAS International Conference on Humanoid Robots, Humanoids 2015 - Seoul, Korea, Republic of
Duration: 2015 Nov 3 → 2015 Nov 5
|IEEE-RAS International Conference on Humanoid Robots
|15th IEEE RAS International Conference on Humanoid Robots, Humanoids 2015
|Korea, Republic of
|15/11/3 → 15/11/5
Bibliographical noteFunding Information:
This work was supported by Korea Institute of Science and Technology (Project no. 2E25280).
© 2015 IEEE.
- Gaussian processes
- Legged locomotion
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Hardware and Architecture
- Human-Computer Interaction
- Electrical and Electronic Engineering