IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i2p690-d720664.html
   My bibliography  Save this article

AI-Based Quantification of Fitness Activities Using Smartphones

Author

Listed:
  • Junhui Huang

    (Department of Civil Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK)

  • Sakdirat Kaewunruen

    (Department of Civil Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK)

  • Jingzhiyuan Ning

    (Department of Civil Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK)

Abstract

To encourage more active activities that have the potential to significantly reduce the risk of people’s health, we aim to develop an AI-based mobile app to identify four gym activities accurately: ascending, cycling, elliptical, and running. To save computational cost, the present study deals with the dilemma of the performance provided by only a phone-based accelerometer since a wide range of activity recognition projects used more than one sensor. To attain this goal, we derived 1200 min of on-body data from 10 subjects using their phone-based accelerometers. Subsequently, three subtasks have been performed to optimize the performances of the K-nearest neighbors (KNN), Support Vector Machine (SVM), Shallow Neural Network (SNN), and Deep Neural Network (DNN): (1) During the process of the raw data converted to a 38-handcrafted feature dataset, different window sizes are used, and a comparative analysis is conducted to identify the optimal one; (2) principal component analysis (PCA) is adopted to extract the most dominant information from the 38-feature dataset described to a simpler and smaller size representation providing the benefit of ease of interpreting leading to high accuracy for the models; (3) with the optimal window size and the transformed dataset, the hyper-parameters of each model are tuned to optimal inferring that DNN outperforms the rest three with a testing accuracy of 0.974. This development can be further implemented in Apps Store to enhance public usage so that active physical human activities can be promoted to enhance good health and wellbeing in accordance with United Nation’s sustainable development goals.

Suggested Citation

  • Junhui Huang & Sakdirat Kaewunruen & Jingzhiyuan Ning, 2022. "AI-Based Quantification of Fitness Activities Using Smartphones," Sustainability, MDPI, vol. 14(2), pages 1-19, January.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:2:p:690-:d:720664
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/2/690/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/2/690/
    Download Restriction: no
    ---><---

    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:gam:jsusta:v:14:y:2022:i:2:p:690-:d:720664. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.