IDEAS home Printed from https://ideas.repec.org/a/epw/ejece0/v6y2022i2id19433.html

Improving the Performance of Closet-Set Classification in Human Activity Recognition by Applying a Residual Neural Network Architecture

Author

Listed:
  • Daniel Rodriguez Gonzalez

    (Colorado Technical University, USA)

  • Yanzhen Qu

    (Colorado Technical University, USA)

Abstract

The Time Series Classification Residual Network (TSCResNet) deep learning model is introduced in this study to improve the classification performance in the human activity recognition (HAR) problem. Specifically in the context of closed-set classification where all labels or classes are present during the model training phase. This contrasts with open-set classification where new, unseen activities are introduced to the HAR system after the training phase. The proposed TSCResNet model is evaluated with the benchmark PAMAP2 Physical Activity Monitoring Dataset. By using the same quantitative methods and data preprocessing protocols as previous research in the field of closed-set HAR, results show that the TSCResNet model architecture is able to achieve improved classification results across accuracy, the weighted F1-score, and mean F1-score.

Suggested Citation

  • Daniel Rodriguez Gonzalez & Yanzhen Qu, 2022. "Improving the Performance of Closet-Set Classification in Human Activity Recognition by Applying a Residual Neural Network Architecture," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 6(2), pages 59-65, March.
    • Handle: RePEc:epw:ejece0:v:6:y:2022:i:2:id:19433
    DOI: 10.24018/ejece.2022.6.2.433
    as

    Download full text from publisher

    File URL: https://eu-opensci.org/index.php/ejece/article/view/19433
    File Function: Abstract page
    Download Restriction: no
    File URL: https://eu-opensci.org/index.php/ejece/article/download/19433/11229
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.24018/ejece.2022.6.2.433?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:epw:ejece0:v:6:y:2022:i:2:id:19433. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: support (email available below). General contact details of provider: https://eu-opensci.org/index.php/ejece .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.