• DocumentCode
    3684511
  • Title

    Motion trajectory analysis for evaluating the performance of functional upper extremity tasks in daily living: a pilot study

  • Author

    Saiyi Li;Pubudu N. Pathirana;Mary P. Galea

  • Author_Institution
    School of Engineering, Deakin Univerisity, Australia
  • fYear
    2015
  • Firstpage
    2701
  • Lastpage
    2704
  • Abstract
    Since 1998, tele-rehabilitation has been extensively studied for its potential capacity of saving time and cost for both therapists and patients. However, one gap hindering the deployment of tele-rehabilitation service is the approach to evaluate the outcome after tele-rehabilitation exercises without the presence of professional clinicians. In this paper, we propose an approach to model jerky and jerky-free movement trajectories with hidden Markov models (HMMs). The HMMs are then utilised to identify the jerky characteristics in a motion trajectory, thereby providing the number and amplitude of jerky movements in the specific length of the trajectory. Eventually, the ability of performing functional upper extremity tasks can be evaluated by classifying the motion trajectory into one of the pre-defined ability levels by looking at the number and amplitude of jerky movements. The simulation experiment confirmed that the proposed method is able to correctly classify motion trajectories into various ability levels to a high degree.
  • Keywords
    "Trajectory","Hidden Markov models","Shape","Computational modeling","Extremities","Yttrium","Kernel"
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE
  • ISSN
    1094-687X
  • Electronic_ISBN
    1558-4615
  • Type

    conf

  • DOI
    10.1109/EMBC.2015.7318949
  • Filename
    7318949