Design and testing of a genetic algorithm neural network in the assessment of gait patterns

It is important to be able to quantify changes in gait pattern accurately in order to understand the clinical implications of surgery or rehabilitation. Although supervised feed-forward backpropagation neural networks are very efficient in many pattern-recognition tasks, the genetic algorithm neural network (GANN), which can search in some appropriate space, has not been used previously for gait-pattern recognition. This study discusses how to use the GANN approach in gait-pattern recognition, and evaluates the complexity and training strategy of the particular classification problem. Both the GANN and a traditional artificial neural network (ANN) were used to classify the gait patterns of patients with ankle arthrodesis and normal subjects. The GANN model was able to classify subjects with recognition rates of up to 98.7%. In contrast, the ANN trained by using all possible predictor variables was only able to classify the subjects with recognition rates of 89.7%. It is suggested that the GANN model is more suitable to exploit the patient's gait pattern. The value of the neuron output can be used as an index of the difference from normal. By this means, all pathological gait patterns may be presented quantitatively.