## 2 Materials and Methods
### Machine Learning for Gesture Discrimination  
Machine learning method was used to recognize the sEMG signals of 6 different gestures. When a subject repeats a gesture, the signal obtained is called an event. Each subject had at least 20 events for each gesture, with an interval of 2-3 s. Overall, we had at least 2400 events in the whole experiment and at least 400 events per gesture (20 subjects \(\times\) At least 20 repetition). From the feature space, three types of classification algorithms were trained for the 6 hand gestures of interest: k-NN, SVM and ANN. In these supervised algorithms (kNN, SVM and ANN), 80% of the dataset was used as the training set of the algorithm for parameter tuning and model fitting; 20% of the dataset was used as the test set of the algorithm to perform the evaluation of the prediction performance of the algorithm. In addition, 10-fold cross validation and grid search methods were used to determine the optimal number of the nearest neighbors for the k-NN classifier and the optimal combination of kernel functions and penalty coefficients for the SVM classifier to achieve the optimal prediction of the classifier. On the other hand, for the ANN classifier, we use different training algorithms and activation functions varying between 2 and 3 hidden layers to achieve the optimal prediction of the classifier. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Domain** & **Feature** & **Formulation** \\ \hline Time & Integral Electromyographic (iEMG) & \(\int_{t}^{t+1}\)EMG(t)\(\ast\)dt \\ Time & Mean Absolute Value (MAV) & \(\frac{1}{N}\sum\limits_{i=1}^{N}\left|\mathbf{x}_{i}\right|\) \\ Time & Input Contribution Rate (ICRi) & \(\frac{\int_{t}^{t+1}\)EMG(t)\(\ast\)dt}{\(\sum_{i=1}^{N}\int_{t}^{t+1}\left|\mathbf{EMG(t)}\right|\ast\)dt} \\ Time & Variance (VAR) & \(\frac{1}{N-1}\sum\limits_{i=1}^{N}\mathbf{x}_{i}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Time features extracted from the EMG signal. In particular, two types of machine learning algorithms that considered sex differences in muscles were proposed. One approach is the distinction of sex datasets. The iEMG values of surface EMG signals from 10 males and 10 females were divided into two different datasets for model training and prediction. At the same time, the iEMG values of a total of 10 subjects (5 males and 5 females) were randomly selected as the control dataset for model training and prediction. Another approach was to add a sex label to the total dataset. The sex label was added to the dataset and used as an additional feature value for classification prediction. The iEMG values of surface EMG signals from 10 males and 10 females were used as an overall dataset, with the dataset in which the sex label was added as the experimental group and the dataset in which the sex label was not added as the control group. The dataset names and dataset descriptions can be found specifically in Table 2. The optimal configurations of k-NN, SVM and ANN classifiers applied to different datasets identified by the training set are shown in Tables 3 and 4. In addition, the method of cross validation (CV) was used to ensure the validity of the results. CV is a method of dividing a dataset into two sets, a \"training' and a'sample' set, for supervised learning. k-fold CV starts by obtaining k subsets with the same number of members from the dataset, with the members of each subset being shared randomly. In a classification process, each subset is used as the training set and all other subsets are used as