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
Link To Document