IDEAS home Printed from https://ideas.repec.org/a/ids/ijenma/v16y2025i2p147-161.html
   My bibliography  Save this article

Machine learning-based approach to identify human push recovery using GAIT analysis

Author

Listed:
  • S. Niranjani
  • Murugesan Punniyamoorthy
  • G. Lakshmi

Abstract

The authors have performed empirical mode decomposition for a process to arrive at the human push recovery data using features that are obtained from intrinsic mode functions (IMFs). For the above purpose data related to leg joint angles (hip, knee and ankle) are collected. Three kinds of pushes were applied to analyse the recovery mechanism, in the field of robotics. The classification was performed using deep neural network (DNN) and other classification methods like KNN, naive Bayes, decision tree, random forest and support vector machine. The results have been compared to find the best method for further analysis. Use of five different classification technique and extraction of two additional features which improved the accuracy of the system are some of the unique features of this article.

Suggested Citation

  • S. Niranjani & Murugesan Punniyamoorthy & G. Lakshmi, 2025. "Machine learning-based approach to identify human push recovery using GAIT analysis," International Journal of Enterprise Network Management, Inderscience Enterprises Ltd, vol. 16(2), pages 147-161.
    • Handle: RePEc:ids:ijenma:v:16:y:2025:i:2:p:147-161
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=146312
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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:ids:ijenma:v:16:y:2025:i:2:p:147-161. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=187 .

    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.