Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms.

Journal: Gait & posture
Published Date: Jun 24, 2021

Abstract

PURPOSE: Machine-learning (ML) approaches have been repeatedly coupled with raw accelerometry to classify physical activity classes, but the features required to optimize their predictive performance are still unknown. Our aim was to identify appropriate combination of feature subsets and prediction algorithms for activity class prediction from hip-based raw acceleration data.

Authors

  • Joana Chong
    Faculty of Sciences, University of Lisbon, Lisbon, Portugal; Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.
  • Petra Tjurin
  • Maisa Niemelä
    Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Infotech, University of Oulu, Oulu, Finland; Medical Research Center, Oulu University Hospital and University of Oulu, Oulu, Finland.
  • Timo Jämsä
    Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Infotech, University of Oulu, Oulu, Finland; Medical Research Center, Oulu University Hospital and University of Oulu, Oulu, Finland; Diagnostic Radiology, Oulu University Hospital, Oulu, Finland.
  • Vahid Farrahi
    Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland. Electronic address: Vahid.farrahi@oulu.fi.

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