Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30447
Title: Support vector machine for classification of walking conditions of persons after stroke with dropped foot
Authors: Lau, HY
Tong, KY
Zhu, H
Keywords: Gait analysis
Locomotion
Stroke
Issue Date: 2009
Publisher: North-Holland
Source: Human movement science, 2009, v. 28, no. 4, p. 504-514 How to cite?
Journal: Human movement science 
Abstract: Walking with dropped foot represents a major gait disorder, which is observed in hemiparetic persons after stroke. This study explores the use of support vector machine (SVMs) to classify different walking conditions for hemiparetic subjects. Seven participants with dropped foot (category 4 of functional ambulatory category) walked in five different conditions: level ground, stair ascent, stair descent, upslope, and downslope. The kinematic data were measured by two portable sensor units, each comprising an accelerometer and gyroscope attached to the lower limb on the shank and foot segments. The overall classification accuracy of stair ascent, stair descent, and other walking conditions was 92.9% using input features from the sensor attached to the shank. It was further improved to 97.5% by adding two more inputs from the sensor attached to the foot. Stair ascent was also classified by the inputs from the foot sensor unit with 96% accuracy. The performance of an SVM was shown to be superior to that of other machine learning methods using artificial neural networks (ANN) and radial basis function neural networks (RBF). The results suggested that the SVM classification method could be applied as a tool for pathological gait analysis, pattern recognition, control signals in functional electrical stimulation (FES) and rehabilitation robot, as well as activity monitoring during rehabilitation of daily activities.
URI: http://hdl.handle.net/10397/30447
ISSN: 0167-9457
DOI: 10.1016/j.humov.2008.12.003
Appears in Collections:Journal/Magazine Article

SFX Query Show full item record

SCOPUSTM   
Citations

30
Last Week
0
Last month
0
Citations as of Dec 17, 2017

WEB OF SCIENCETM
Citations

26
Last Week
0
Last month
0
Citations as of Dec 17, 2017

Page view(s)

58
Last Week
2
Last month
Citations as of Dec 11, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.