Wrist accelerometer shape feature derivation methods for assessing activities of daily living.

Journal: BMC medical informatics and decision making
Published Date: Dec 12, 2018

Abstract

BACKGROUND: There has been an increasing interest in understanding the usefulness of wrist-based accelerometer data for physical activity (PA) assessment due to the ease of use and higher user compliance than other body placements. PA assessment studies have relied on machine learning methods which take accelerometer data in forms of variables, or feature vectors.

Authors

  • Matin Kheirkhahan
    Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA. matin@cise.ufl.edu.
  • Avirup Chakraborty
    Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.
  • Amal A Wanigatunga
    Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA.
  • Duane B Corbett
    Department of Aging and Geriatric Research, University of Florida, Gainesville, FL, USA.
  • Todd M Manini
    Department of Aging and Geriatric Research, University of Florida, Gainesville, FL, USA.
  • Sanjay Ranka
    Dept. of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, USA, ximen14@ufl.edu, anand@cise.ufl.edu, ranka@cise.ufl.edu.

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