Knee torque estimation in non-pathological gait using dynamics modelling and feed-forward neural network

This paper presents a knee torque estimation in non-pathological gait cycle at stance phase. Comparative modelling by using dynamics model and neural network model is discussed. Dynamics modelling is constructed by using simple two degree of freedom dynamics with Newtonian calculation approach and more complex four degree of freedom dynamics with Lagrangian calculation approach. Neural network based model is constructed with feed-forward neural network (FNN) structure using six different kinematic and kinetic input from gait experiments to provide one output in the form of knee joint torque. Six cases of different combination of input is presented to test which model give lower complexity in input data while maintain the performance criterion. The available dataset used for simulation was also divided into five different speed categories. The performance of FNN model simulation is given by the normalized root mean squared error (NRMSE). We find that in estimating knee torque, hip angle data is noticeably insignificant, while ankle angle data is more important than ground reaction force in medio-lateral and antero-posterior direction combined. Using only two kinematics input and one kinetic input can achieve 2.67% of NRMSE.