Application of Raw Accelerometer Data and Machine-Learning Techniques to Characterize Human Movement Behavior: A Systematic Scoping Review.

Journal: Journal of physical activity & health
Published Date: Mar 1, 2020

Abstract

BACKGROUND: Application of machine learning for classifying human behavior is increasingly common as access to raw accelerometer data improves. The aims of this scoping review are (1) to examine if machine-learning techniques can accurately identify human activity behaviors from raw accelerometer data and (2) to summarize the practical implications of these machine-learning techniques for future work.

Authors

  • Anantha Narayanan
    School of Sport and Recreation, Auckland University of Technology, Auckland, NEW ZEALAND.
  • Farzanah Desai
  • Tom Stewart
    a Sport Performance Research Institute New Zealand , AUT University , Auckland , New Zealand.
  • Scott Duncan
    School of Sport and Recreation, Auckland University of Technology, Auckland, NEW ZEALAND.
  • Lisa Mackay
    School of Sport and Recreation, Auckland University of Technology, Auckland, NEW ZEALAND.

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