Analyzing the impact of activation functions on the performance of the data-driven gait model

Long-term prediction of joint kinematics is an important factor for the development of advanced technology in the area of the prosthetic leg, biped robot, automotive industry, and human-robot collaboration. It is well-defined that the long-term prediction of joint kinematic angle for an uneven surface is a challenging task. In previous work, authors have employed deep learning techniques for kinematic modelling using the collected walking dataset on an uneven surface with multiple speeds. However, it is a challenging task to find the appropriate activation function for deep learning techniques. Therefore, this research work focuses on the suitability of the activation function for a data-driven gait model for multiple slopes and inclines as ground conditions. Twenty-five activation function with their time complexity is extensively studied. In addition, the fusion of standard error from the subject mean trajectory with conventional loss function is also presented. This fusion helps the training algorithm to train the data-driven model to follow the low variance point in the gait cycle more precisely. The result shows that the Sigmoid-weighted Linear Units (SiLU) activation function-based gait model outperforms the others based on the maximum error summary statistic. It is also established that the SiLU function-based gait model trained by fused objective function significantly improved the long-term prediction for two cases: (a) step change slope and (b) continuously varying slope.