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 :
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