SEMG based intention identification of complex hand motion using nonlinear time series analysis

Yaxu Xue, Zhaojie Ju

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

This paper proposes a hand motion recognition system for classifying different complex hand motions based on Surface Electromyography (SEMG). By defining ten common hand motions, the SEMG signals are recorded based on a SEMG capture device. A series of signal processing methods, including signal denoising, and feature extraction are analyzed to acquire the SEMG features. A trained Random Forest (RF) algorithm is used for the classification of ten different hand motions. The experimental results show that the proposed hand motion recognition system has a higher classification accuracy for identifying different hand motions.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
PublisherIEEE
Pages357-361
Number of pages5
ISBN (Electronic)978-1-5386-1728-1
ISBN (Print)978-1-5386-1729-8
DOIs
Publication statusPublished - 16 Sep 2019

Publication series

Name2019 9th International Conference on Information Science and Technology (ICIST)
PublisherIEEE
ISSN (Print)2573-3311

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