• Login
    View Item 
    •   Home
    • Research
    • Conference Papers
    • View Item
    •   Home
    • Research
    • Conference Papers
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    A Position Weight Matrix Feature Extraction Algorithm Improves Hand Gesture Recognition

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Feature of.pdf
    Size:
    1.182Mb
    Format:
    PDF
    Description:
    Accepted manuscript
    Download
    Type
    Conference Paper
    Authors
    Chahid, Abderrazak cc
    Khushaba, Rami
    Al-Jumaily, Adel
    Laleg-Kirati, Taous-Meriem cc
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Estimation, Modeling and ANalysis Group
    Date
    2020-08-28
    Online Publication Date
    2020-08-28
    Print Publication Date
    2020-07
    Permanent link to this record
    http://hdl.handle.net/10754/665147
    
    Metadata
    Show full item record
    Abstract
    Recent advances in the biomedical field have generated a massive amount of data and records (signals) that are collected for diagnosis purposes. The correct interpretation and understanding of these signals present a big challenge for digital health vision. In this work, Quantization-based position Weight Matrix (QuPWM) feature extraction method for multiclass classification is proposed to improve the interpretation of biomedical signals. This method is validated on surface Electromyogram (sEMG) signals recognition for eight different hand gestures. The used CapgMyo dataset consists of high-density sEMG signals across 128 channels acquired from 9 intact subjects. Our pilot results show that an accuracy of up to 83% can be achieved for some subjects using a support vector machine classifier, and an average accuracy of 75% has been reached for all studied subjects using the CapgMyo dataset. The proposed method shows a good potential in extracting relevant features from different biomedical signals such as Electroencephalogram (EEG) and Magnetoencephalogram (MEG) signals.
    Citation
    Chahid, A., Khushaba, R., Al-Jumaily, A., & Laleg-Kirati, T.-M. (2020). A Position Weight Matrix Feature Extraction Algorithm Improves Hand Gesture Recognition. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). doi:10.1109/embc44109.2020.9176097
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Conference/Event name
    2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
    ISBN
    978-1-7281-1991-5
    DOI
    10.1109/EMBC44109.2020.9176097
    Additional Links
    https://ieeexplore.ieee.org/document/9176097/
    https://ieeexplore.ieee.org/document/9176097/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9176097
    ae974a485f413a2113503eed53cd6c53
    10.1109/EMBC44109.2020.9176097
    Scopus Count
    Collections
    Conference Papers; Electrical and Computer Engineering Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2023  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.