@inproceedings{ac20e41f35a4453ea9b888840bcd7ac5,
title = "The analysis of pedestrian{\textquoteright}s motion state based on the entropy of sEMG",
abstract = "In order to solve the problem that traditional eigenvalue analysis of time domain and frequency domain cannot classify pedestrian{\textquoteright}s motion state correctly in pedestrian navigation because of the changing of stride frequency and length, etc., a method for motion state analysis in combination with time domain characteristics and entropy of sEMG was presented. This method analyzed the complexity and amplitude of pedestrian{\textquoteright}s motion state, estimated the stride frequency, and classified four movement state include walking, running, upstairs, and downstairs according to variation feature of sEMG. The experimental results show that the average classification accuracy went up at least by 8.25 \% with presented method, it gets more reliable and stronger anti-interference capacity compared with the characteristics of time domain.",
keywords = "Entropy, Feature extraction, Pedestrian{\textquoteright}s motion state, sEMG",
author = "Min Li and Long Zhao",
note = "Publisher Copyright: {\textcopyright} Springer Science+Business Media Singapore 2016.; 7th China Satellite Navigation Conference, CSNC 2016 ; Conference date: 18-05-2016 Through 20-05-2016",
year = "2016",
doi = "10.1007/978-981-10-0934-1\_26",
language = "英语",
isbn = "9789811009334",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "287--297",
editor = "Jingnan Liu and Shiwei Fan and Jiadong Sun and Feixue Wang",
booktitle = "China Satellite Navigation Conference, CSNC 2016, Proceedings",
address = "德国",
}