Design of a neural modelling scheme for gait temporal features

This paper represents an artificial neural network that captures knee angle variations for adult gait scenarios. Back propagation algorithm is used to train the neural network. The data set that are needed for training have been obtained artificially. Gait cycle is analysed in eight different phases. With the neural network model, the phase and the subsequent angle value are predicted The suggested neural network model is trained for different inclinations and walking speed, the results are recorded and discussed.