• DocumentCode
    104745
  • Title

    Adaptive Interface for Personalized Center of Mass Self-Identification in Home Rehabilitation

  • Author

    Gonzalez, Alejandro ; Fraisse, Philippe ; Hayashibe, Mitsuhiro

  • Author_Institution
    LIRMM, Univ. of Montpellier, Montpellier, France
  • Volume
    15
  • Issue
    5
  • fYear
    2015
  • fDate
    May-15
  • Firstpage
    2814
  • Lastpage
    2823
  • Abstract
    As the center of mass (CoM) position can be used to determine stability, current rehabilitation standards may be improved by tracking it. A personalized CoM estimate can be obtained using the statically equivalent serial chain (SESC) once the model parameters are identified. The identification phase can be completed using low-cost sensors (Kinect and Wii balance board) outside the laboratory making CoM estimation feasible in a patient´s home. This paper focuses on: 1) improving the SESC identification quality and speed and 2) using the estimated CoM to determine stability. Identification time is reduced by creating a visual adaptive interface where the subject´s limbs are colored based on the convergence of the SESC parameters. A study was conducted on eight subjects and showed a faster convergence and lower root mean square error (RMSE) when the adaptive interface was used. We found that a model capable of estimating the CoM position with an RMSE of 27 mm could be obtained after only 90 s of identification when the interface was used, whereas twice as much time was needed when the interface was not used. The interface that was developed can be used by a subject to track his/her CoM position in a self-directed way. Stability was determined for a squat task using a dynamic index obtained from the estimated CoM trajectory and using only Kinect measurements. This shows one potential application for home rehabilitation and monitoring.
  • Keywords
    mean square error methods; patient monitoring; patient rehabilitation; statistical analysis; Kinect measurements; RMSE method; SESC identification quality; SESC parameters; adaptive interface; dynamic index; home monitoring; home rehabilitation; low root mean square error method; low-cost sensors; model parameters; personalized CoM estimation; personalized center of mass self-identification; phase identification; squat task; statiscally equivalent serial chain; visual adaptive interface; Estimation; Kalman filters; Sensors; Skeleton; Training; Trajectory; Visualization; Center of mass; Kalman filter; adaptive identification; biomechanics; home rehabilitation; postural stability; real time feedback; subject-specific modeling;
  • fLanguage
    English
  • Journal_Title
    Sensors Journal, IEEE
  • Publisher
    ieee
  • ISSN
    1530-437X
  • Type

    jour

  • DOI
    10.1109/JSEN.2014.2379431
  • Filename
    6994746