IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v32y2023i5d10.1007_s10260-023-00696-z.html
   My bibliography  Save this article

Improving state estimation through projection post-processing for activity recognition with application to football

Author

Listed:
  • Michał Ciszewski

    (Delft University of Technology)

  • Jakob Söhl

    (Delft University of Technology)

  • Geurt Jongbloed

    (Delft University of Technology)

Abstract

The past decade has seen an increased interest in human activity recognition based on sensor data. Most often, the sensor data come unannotated, creating the need for fast labelling methods. For assessing the quality of the labelling, an appropriate performance measure has to be chosen. Our main contribution is a novel post-processing method for activity recognition. It improves the accuracy of the classification methods by correcting for unrealistic short activities in the estimate. We also propose a new performance measure, the Locally Time-Shifted Measure (LTS measure), which addresses uncertainty in the times of state changes. The effectiveness of the post-processing method is evaluated, using the novel LTS measure, on the basis of a simulated dataset and a real application on sensor data from football. The simulation study is also used to discuss the choice of the parameters of the post-processing method and the LTS measure.

Suggested Citation

  • Michał Ciszewski & Jakob Söhl & Geurt Jongbloed, 2023. "Improving state estimation through projection post-processing for activity recognition with application to football," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(5), pages 1509-1538, December.
    • Handle: RePEc:spr:stmapp:v:32:y:2023:i:5:d:10.1007_s10260-023-00696-z
    DOI: 10.1007/s10260-023-00696-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-023-00696-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10260-023-00696-z?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:spr:stmapp:v:32:y:2023:i:5:d:10.1007_s10260-023-00696-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.