@inproceedings{6a84c603fe6a41b3b8079880696dc24d,
title = "Upper-Body Motion Mode Recognition Based on IMUs for a Dynamic Spine Brace",
abstract = "This paper presents an upper-body motion mode recognition method based on inertial measurement units (IMUs) using cascaded classification approaches and integrated machine learning algorithms. The proposed method is designed to be applied on a dynamic spine brace in the future to assess its usability. This study focuses on the problem of classifying upper-body motion modes by using four IMUs worn on the upper-body of the subjects. Six locomotion modes and ten locomotion transitions were investigated. A quadratic discriminant analysis (QDA) classifier and a support vector machine (SVM) classifier were deployed in our study. With selected cascade classification strategies, the system is demonstrated to achieve a satisfactory performance with an average of 96.77\%(QDA) and 97.64\%(SVM) recognition accuracy. The obtained results prove the effectiveness of the proposed method.",
author = "Sun, \{Pihsaia S.\} and Jingeng Mai and Zhihao Zhou and Sunil Agrawal and Qining Wang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Cyborg and Bionic Systems, CBS 2018 ; Conference date: 25-10-2018 Through 27-10-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/CBS.2018.8612187",
language = "英语",
series = "2018 IEEE International Conference on Cyborg and Bionic Systems, CBS 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "167--170",
booktitle = "2018 IEEE International Conference on Cyborg and Bionic Systems, CBS 2018",
}