Biomechanical monitoring and machine learning for the detection of lying postures.

Journal: Clinical biomechanics (Bristol, Avon)
Published Date:

Abstract

BACKGROUND: Pressure mapping technology has been adapted to monitor over prolonged periods to evaluate pressure ulcer risk in individuals during extended lying postures. However, temporal pressure distribution signals are not currently used to identify posture or mobility. The present study was designed to examine the potential of an automated approach for the detection of a range of static lying postures and corresponding transitions between postures.

Authors

  • Silvia Caggiari
    Skin Health Research Group, School of Health Sciences, Faculty of Environmental and Life Sciences, University of Southampton, SO17 1BJ, United Kingdom. Electronic address: S.Caggiari@soton.ac.uk.
  • Peter R Worsley
    Skin Health Research Group, School of Health Sciences, Faculty of Environmental and Life Sciences, University of Southampton, SO17 1BJ, United Kingdom.
  • Yohan Payan
    University of Grenoble Alpes, CNRS, TIMC-IMAG, Grenoble 38000, France.
  • Marek Bucki
    TexiSense, France.
  • Dan L Bader
    Skin Health Research Group, School of Health Sciences, Faculty of Environmental and Life Sciences, University of Southampton, SO17 1BJ, United Kingdom.