• DocumentCode
    3502604
  • Title

    Neural network analysis of gait biomechanical data for classification of knee osteoarthritis

  • Author

    McBride, J. ; Zhang, S. ; Wortley, M. ; Paquette, M. ; Klipple, G. ; Byrd, E. ; Baumgartner, L. ; Zhao, X.

  • Author_Institution
    Dept. of Mech., Aerosp., & Biomed. Eng., Univ. of Tennessee, Knoxville, TN, USA
  • fYear
    2011
  • fDate
    15-17 March 2011
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Osteoarthritis is a degenerative joint disease, which causes the degradation of articular cartilage and subchondral bone. The disease may result in mechanical abnormalities of the joints, including weight bearing joints such as the knees and hips. In this work, we analyze gait biomechanical data using neural network models to predict the level of joint deterioration and the level of pain in participants suffering from knee osteoarthritis. The results of the analyses demonstrate strong correlation between gait kinetics and joint deterioration and level of pain in osteoarthritic individuals.
  • Keywords
    biological tissues; bone; diseases; gait analysis; medical computing; neural nets; patient diagnosis; articular cartilage degradation; degenerative joint disease; gait biomechanical data; gait kinetics; joint deterioration level prediction; joint mechanical abnormalities; knee osteoarthritis classification; neural network analysis; neural network models; pain level prediction; subchondral bone; weight bearing joints; Accuracy; Artificial neural networks; Biological neural networks; Force; Force measurement; Knee; Pain;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Sciences and Engineering Conference (BSEC), 2011
  • Conference_Location
    Knoxville, TN
  • Print_ISBN
    978-1-61284-411-4
  • Electronic_ISBN
    978-1-61284-410-7
  • Type

    conf

  • DOI
    10.1109/BSEC.2011.5872315
  • Filename
    5872315