*2.4. Estimation Model*

Because of the complexity of the sEMG signal and the differences between subjects, it is hard to establish a general mathematical model to represent the mapping relationship from sEMG to the knee angle. Furthermore, the biomechanical model describing the relationship between the sEMG and joint angle is also complicated and difficult to construct for practical application. Therefore, to establish a universal sEMG–angle model of a human joint, with a learning function, this study adopted a novel model-free method using random forest (RF) in combination with principal component analysis (PCA), in order to set up the estimation model between sEMG signal and knee movement. It is expected that this coupled ML method will be able to handle the estimation issue for different participants with a parametric adaptive approach. The input of the model is the processed sEMG, and the output is the knee angle.
