Surface Emg channel selection for thumb motion classificationsignal

Wan-Fen Xu, Yinfeng Fang, Gongyue Zhang, Zhaojie Ju, Gongfa Li, Honghai Liu

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

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Simplifying the interaction between humans and computers has become intensively important. Handgesture contains large amount information that can facilitate the communication among humans, and it can also be utilized to interact with external devices. As a result, this study aims to decode the different hand gestures from sEMG signal. The thumb plays the most important role in hand-based object manipulation, such as touch screen control for smart phones, for which many thumb-based hand involved. Therefore, studying the relationship between EMG signals and the thumb movement has certain value for the future human-computer interaction. In this paper, we focus on the identification of electrode position. The signal from which is not so related to the thumb movement, and thus these sEMG channels can be reduced. In the experiment, a 16-channels sleeve is utilized and a variance-based method was proposedto identify the redundant channels. It is found that there exist three common redundant channelsacross nine subjects., and all located at the inside of the forearm.
Original languageEnglish
Title of host publication2018 International Conference on Machine Learning and Cybernetics
Subtitle of host publicationICMLC
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)978-1-5386-5214-5
ISBN (Print)978-1-5386-5215-2
Publication statusPublished - 12 Nov 2018
Event2018 International Conference on Machine Learning and Cybernetics -, Chengdu, China
Duration: 15 Jul 201818 Jul 2018

Publication series

NameIEEE ICMLC Proceeding Series
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348


Conference2018 International Conference on Machine Learning and Cybernetics
Abbreviated titleICMLC 2018


  • EMG
  • Gesture Recognition
  • Thumb


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