• DocumentCode
    163022
  • Title

    Human movement quantification using Kinect for in-home physical exercise monitoring

  • Author

    Gauthier, S. ; Cretu, Ana-Maria

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Univ. du Quebec en Outaouais, Gatineau, QC, Canada
  • fYear
    2014
  • fDate
    5-7 May 2014
  • Firstpage
    6
  • Lastpage
    11
  • Abstract
    The paper proposes a framework for in-home physical exercise monitoring based on a Kinect platform. The analysis goes beyond the state-of-the-art solutions by monitoring more joints and offering more advanced reporting capabilities on the movement such as: the position and trajectory of each joint, the working envelope of each body member, the average velocity, and a measure of the user´s fatigue after an exercise sequence. This data can be visualised and compared to a standard (e.g. a healthy user, for rehabilitation purposes) or an ideal performance (e.g. a perfect sport pose for exercising) in order to give the user a measure on his/her own performance and incite his/her motivation to continue the training program. Such information can be used as well by a therapist or professional sports trainer to evaluate the progress of a patient or of a trainee.
  • Keywords
    patient rehabilitation; sensors; telemedicine; Kinect platform; average velocity; human movement quantification; in-home physical exercise monitoring; joint position; joint trajectory; Biomedical monitoring; Elbow; Fatigue; Joints; Knee; Tracking; Kinect; movement quantification; physical exercising; rehabilitation; skeleton tracking;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 2014 IEEE International Conference on
  • Conference_Location
    Ottawa, ON
  • Print_ISBN
    978-1-4799-2613-8
  • Type

    conf

  • DOI
    10.1109/CIVEMSA.2014.6841430
  • Filename
    6841430