Evaluating and Enhancing the Generalization Performance of Machine Learning Models for Physical Activity Intensity Prediction From Raw Acceleration Data.

Journal: IEEE journal of biomedical and health informatics
Published Date: May 20, 2019

Abstract

PURPOSE: To evaluate and enhance the generalization performance of machine learning physical activity intensity prediction models developed with raw acceleration data on populations monitored by different activity monitors.

Authors

  • Vahid Farrahi
    Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland. Electronic address: Vahid.farrahi@oulu.fi.
  • 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.
  • Petra Tjurin
  • Maarit Kangas
    Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Medical Research Center, Oulu University Hospital and University of Oulu, Oulu, Finland.
  • Raija Korpelainen
    Medical Research Center, Oulu University Hospital and University of Oulu, Oulu, Finland; Center for Life Course Health Research, University of Oulu, Oulu, Finland; Oulu Deaconess Institute, Department of Sports and Exercise Medicine, 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.

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