Comparative evaluation of features and techniques for identifying activity type and estimating energy cost from accelerometer data

Rohit J. Kate, Ann M. Swartz, Whitney Allegra Welch, Scott J. Strath

Research output: Contribution to journalArticle

16 Scopus citations


Wearable accelerometers can be used to objectively assess physical activity. However, the accuracy of this assessment depends on the underlying method used to process the time series data obtained from accelerometers. Several methods have been proposed that use this data to identify the type of physical activity and estimate its energy cost. Most of the newer methods employ some machine learning technique along with suitable features to represent the time series data. This paper experimentally compares several of these techniques and features on a large dataset of 146 subjects doing eight different physical activities wearing an accelerometer on the hip. Besides features based on statistics, distance based features and simple discrete features straight from the time series were also evaluated. On the physical activity type identification task, the results show that using more features significantly improve results. Choice of machine learning technique was also found to be important. However, on the energy cost estimation task, choice of features and machine learning technique were found to be less influential. On that task, separate energy cost estimation models trained specifically for each type of physical activity were found to be more accurate than a single model trained for all types of physical activities.

Original languageEnglish (US)
Article number360
Pages (from-to)360-379
Number of pages20
JournalPhysiological Measurement
Issue number3
StatePublished - Feb 10 2016



  • activity identification
  • energy cost estimation
  • feature representation
  • machine learning

ASJC Scopus subject areas

  • Biophysics
  • Physiology
  • Biomedical Engineering
  • Physiology (medical)

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