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sEMG-based hand gesture classification with transient signal

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

Surface electromyography (sEMG) can provide a novel control method for human machine interface (HMI) with the improvement of signal decoding technology. sEMG-based hand gesture recognition is the key part of HMI control strategy. However, unstable and complex daily used scenarios hinder the further development of sEMG-based control strategy. In this paper, we concentrate on the data preprocessing part. Three different signal segments were extracted including the transient signal segments between gestures, standard signal segments and stationary signal segments which is smaller than standard segment. By setting up several experiments to analyze and evaluate the classification performance with these transient information. Our research found that transient signal segments can reflects more effective information than the stationary signals in inter-subject scenes. It gained more classification accuracy and stability. In addition, it also performance better in other two scenes in ten hand gesture recognition in intra-session and inter-session.
Original languageEnglish
Title of host publicationICRRI 2020: Robotics and Rehabilitation Intelligence
Subtitle of host publicationFirst International Conference, ICRRI 2020, Fushun, China, September 9–11, 2020, Proceedings, Part II
EditorsJianhua Qian, Honghai Liu, Jiangtao Cao, Dalin Zhou
Number of pages12
ISBN (Electronic)978-981-33-4932-2
ISBN (Print)978-981-33-4931-5
Publication statusPublished - 3 Jan 2021
Event1st International Conference on Robotics and Rehabilitation Intelligence - Fushun, China
Duration: 9 Sep 202011 Sep 2020

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer, Singapore
ISSN (Print)1865-0929


Conference1st International Conference on Robotics and Rehabilitation Intelligence
Abbreviated titleICRRI 2020


  • ICRRI2020_214_final_v2-2

    Rights statement: This is a post-peer-review, pre-copyedit version of an article published in Qian J., Liu H., Cao J., Zhou D. (eds) Robotics and Rehabilitation Intelligence. ICRRI 2020. Communications in Computer and Information Science, vol 1336. The final authenticated version is available online at:

    Accepted author manuscript (Post-print), 825 KB, PDF document

    Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 18/12/21

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