@inproceedings{203b7bcf78d14b43ac512f5e37262f7f,
title = "Classifier Selection for Locomotion Mode Recognition Using Wearable Capacitive Sensing Systems",
abstract = "Capacitive sensing has been proven valid for locomotion mode recognition as an alternative of popular electromyography based methods in the control of powered prostheses. In this paper, we analyze the characteristics of the capacitive signals and extract suitable feature sets to improve the recognition accuracy. Then the classification results of different classifiers are compared and one optimal classifier which can offer highest accuracy within a reasonable time limit is selected. Experimental results show that the recognition accuracy of the wearable capacitive sensing system has been improved by using the selected classifier.",
keywords = "Capacitive sensing, classifier selection, locomotion mode recognition, lower-limb prostheses, wearable systems",
author = "Yi Song and Yating Zhu and Enhao Zheng and Fei Tao and Qining Wang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 2nd International Conference on Robot Intelligence Technology and Applications, RiTA 2013 ; Conference date: 18-12-2013 Through 20-12-2013",
year = "2014",
doi = "10.1007/978-3-319-05582-4\_67",
language = "英语",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "763--774",
editor = "Fakhri Karray and Matson, \{Eric T.\} and Hyun Myung and Jong-Hwan Kim and Matson, \{Eric T.\} and Peter Xu",
booktitle = "Robot Intelligence Technology and Applications 2 - Results from the 2nd International Conference on Robot Intelligence Technology and Applications",
}