Walking Gait Phase Detection Based on Acceleration Signals Using Voting-Weighted Integrated Neural Network
Human gait phase recognition is a significant technology for rehabilitation training robot, human disease diagnosis, artificial prosthesis, and so on. The efficient design of the recognition method for gait information is the key issue in the current gait phase division and eigenvalues extraction re...
Saved in:
Main Authors: | Lei Yan, Tao Zhen, Jian-Lei Kong, Lian-Ming Wang, Xiao-Lei Zhou |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2020-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2020/4760297 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Hybrid Deep-Learning Framework Based on Gaussian Fusion of Multiple Spatiotemporal Networks for Walking Gait Phase Recognition
by: Tao Zhen, et al.
Published: (2020-01-01) -
A Weighted Voting Classifier Based on Differential Evolution
by: Yong Zhang, et al.
Published: (2014-01-01) -
Evaluation of Gait Performance of a Hemipelvectomy Amputation Walking with a Canadian Prosthesis
by: M. T. Karimi, et al.
Published: (2014-01-01) -
A Low-Cost Anthropometric Walking Robot for Reproducing Gait Lab Data
by: Rogério Eduardo da Silva Santana, et al.
Published: (2008-01-01) -
Impaired Economy of Gait and Decreased Six-Minute Walk Distance in Parkinson's Disease
by: Leslie I. Katzel, et al.
Published: (2012-01-01)