Natural gait parameters prediction for gait rehabilitation via artificial neural network

Gait pattern planning is an important issue in robotic gait rehabilitation. Gait pattern is known to be related to gait parameters, such as cadence, stride length, and walking speed. Thus, prior before the discussion of gait pattern planning, the planning of gait parameters for natural walking should be addressed. This work utilizes multi-layer perceptron neural network (MLPNN) to predict natural gait parameters for a given subject. The inputs of the MLPNN are age, gender, body height, and body weight of the targeted subject. The MLPNN is trained to output a suitable walking speed and cadence for given subject. Two MLPNNs are trained to study the efficiency and accuracy in predicting the desired outputs, for two different setups. First setup is that the MLPNN is trained specifically for slow speed condition only. In second setup, the MLPNN is trained for both slow and normal speed conditions. The results of the MLPNNs are presented in this paper. The efficiency and accuracy of the MLPNNs are discussed.