Potential of the back propagation neural network in the assessment of gait patterns in ankle arthrodesis.
OBJECTIVE
The purpose of this study was to recognize gait pattern in ankle arthrodesis by using a neural network trained with time domain input and compare the performance of the neural network with the statistical method.


METHODS
Three-layered feed-forward back propagation neural network and a statistical method were used to classify gait patterns of patients with ankle arthrodesis and normal subjects.


BACKGROUND
Although backpropagation neural networks are very efficient in many pattern recognition tasks, they have not been used for gait pattern recognition of ankle arthrodesis.


METHODS
A total of eighteen parameters measured by force platforms, including nine force parameters and their chronologic incidence of occurrence, were used to classify gait patterns.


RESULTS
The results showed that the neural network model was able to classify subjects with recognition rates up to 95.8%. In contrast, the statistical method was only able to classify the subjects with recognition rates of 91.5%.


CONCLUSIONS
The backpropagation neural network method has better accuracy than the statistical method in discriminating subjects and the time domain features carry important prognostic information.


CONCLUSIONS
It is important to be able to quantify the changes in gait pattern after arthrodesis to understand the clinical implications of arthrodesis.
Wu
W L
WL
Institute of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan, ROC.
Su
F C
FC
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D016512:Ankle Injuries / Q000503:physiopathology / Q000601:surgery; D000843:Ankle Joint / Q000502:physiology* / Q000601:surgery; D001168:Arthritis / Q000503:physiopathology / Q000601:surgery; D001174:Arthrodesis; D016002:Discriminant Analysis; D005684:Gait / Q000502:physiology*; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012372:ROC Curve; D012680:Sensitivity and Specificity