*2.5. Arguments Selection*

For comparison with RFPCA in this study, another method, namely BPPCA, was also presented. This method is similar to RFPCA in Section 2.4.3, with a substitution of a conventional BP for RF. For different methods, there are different arguments regarding the effect they have on the performance of the estimation. In a model of RF, there are three main parameters to be determined, namely the number of trees in the forest (*N*), the number of features of the input (*Nf*) and the minimum size of the terminal nodes (*Nm*). The parameters for the BP are the number of epochs (*Ne*), the number of hidden layer nodes (*Nh*), the learning rate (*Lr*) and the learning goal (*Lg*).

During the test of the ML methods, the results showed that the performance of motion estimation was not sensitive to the difference of individuals using the same parameters, which was similar to the results found in [19]. Thus, it meant that universal values could be chosen from these parameters. Referring to the parameter settings in [19], and to reach a compromise between estimation accuracy and computational time, the parameters for our methods were set as recorded in Table 2.

**Table 2.** The values of parameters of the machine learning (ML) methods.


To study the historic effect of previous sEMG, the data from one trail with 4 GCs from each subject would be introduced. A total of 75% of the data was used for training and the rest was used for the testing set.

For the study of the sample size, as noted, 61 GDs were utilized for the estimation work of one subject, and the last GD, with approximately 128 samples, was always the testing set. Hereby, parameter *Ss* in the interval of [1, 60] was defined to represent the sample size. For example, when *Ss* = 1, the 60th GD would be the training set, when *Ss* = 2, the 60th and the 59th GDs were the training set, and by that logic, when *Ss* was 60, all of the 60 GDs would be used for training. The sample size was nearly proportional to the *Ss*.
