Markerless human motion analysis in Gauss–Laguerre transform domain: an application to sit-to-stand in young and elderly people

  • Authors:

  • Michela Goffredo;Maurizio Schmid;Silvia Conforto;Marco Carli;Alessandro Neri;Tommaso D’Alessio

  • Affiliations:

  • School of Electronics and Computer Science, University of Southampton, Southampton, U.K. and Department of Applied Electronics, University Roma Tre, Rome, Italy;Department of Applied Electronics, University Roma Tre, Rome, Italy and Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA;Department of Applied Electronics, University Roma Tre, Rome, Italy;Department of Applied Electronics, University Roma Tre, Rome, Italy and Image Processing Laboratory, University of California, Santa Barbara, CA;Department of Applied Electronics, University Roma Tre, Rome, Italy;Department of Applied Electronics, University Roma Tre, Rome, Italy

  • Venue:
  • IEEE Transactions on Information Technology in Biomedicine
  • Year:
  • 2009

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Abstract

A markerless computer vision technique specifically designed to track natural elements on the human body surface is presented. The method implements the estimate of translation, rotation, and scaling by means of a maximum likelihood approach carried out in the Gauss-Laguerre transform domain. The approach is particularly suitable for human movement analysis in clinical contexts, where kinematics is at present performed by means of marker-based systems. Specific drawbacks of these latter systems, such as the burden of time for marker placement and the intrinsic intrusive nature, would be removed by the proposed method. Experimental results in terms of tracking performance are obtained by analyzing video sequences capturing the execution of the sit-to-stand task in two groups of young and elderly volunteers. The results are compared with clinical studies that used marker-based systems, and are particularly encouraging for a future extension of the approach to other motor tasks and to predict scores obtained from the physical performance batteries that are widely and regularly used by clinicians and physical therapists.