Correlation Engine 2.0
Clear Search sequence regions

  • algorithms (2)
  • foot (1)
  • grain (1)
  • humans (1)
  • investigates (1)
  • living (1)
  • motor activity (1)
  • thigh (1)
  • tri (1)
  • wrist (1)
  • young adult (1)
  • Sizes of these terms reflect their relevance to your search.

    This article describes an investigation to determine the optimal placement of accelerometers for the purpose of detecting a range of everyday activities. The paper investigates the effect of combining data from accelerometers placed at various bodily locations on the accuracy of activity detection. Eight healthy males participated within the study. Data were collected from six wireless tri-axial accelerometers placed at the chest, wrist, lower back, hip, thigh and foot. Activities included walking, running on a motorized treadmill, sitting, lying, standing and walking up and down stairs. The Support Vector Machine provided the most accurate detection of activities of all the machine learning algorithms investigated. Although data from all locations provided similar levels of accuracy, the hip was the best single location to record data for activity detection using a Support Vector Machine, providing small but significantly better accuracy than the other investigated locations. Increasing the number of sensing locations from one to two or more statistically increased the accuracy of classification. There was no significant difference in accuracy when using two or more sensors. It was noted, however, that the difference in activity detection using single or multiple accelerometers may be more pronounced when trying to detect finer grain activities. Future work shall therefore investigate the effects of accelerometer placement on a larger range of these activities.


    Ian Cleland, Basel Kikhia, Chris Nugent, Andrey Boytsov, Josef Hallberg, Kåre Synnes, Sally McClean, Dewar Finlay. Optimal placement of accelerometers for the detection of everyday activities. Sensors (Basel, Switzerland). 2013 Jul 17;13(7):9183-200

    Expand section icon Mesh Tags

    PMID: 23867744

    View Full Text