# Review of sEMG for Robot Control: Techniques and Applications
## 3 Processing and Pattern Recognition Methods
### Pattern Recognition  
The data obtained through the above processing should be input into a classifier for pattern recognition that can extract human actions from sEMG signals through machine-learning (ML) methods. The classifier can then be applied to specific scenes, such as HMIs or prosthesis control. Academic research has focused on the type of classifier used and how to improve the accuracy of classification. In recent studies, Support Vector Machines (SVMs), Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), Decision Trees (DTs), Random Forest (RF), the Hidden Markov Model (HMM), Bayesian Classifiers (BCs), Fuzzy Control (FC), Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Artificial Neural Networks (ANNs) as well as their improved models have been used to identify sEMG. The performance of different classifiers in different scenarios may vary significantly when using machine-learning methods. Therefore, in recent studies, researchers have used various methods to simultaneously compare and select those with better performance. After extracting six TD features from the same dataset, a SVM, LDA, and KNN were \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Pre-Processing *** & **Feature Sets *** & **Classifiers *** & **Publish Time** \\ \hline
[117] & - & RMS & Fuzzy, NN & 2013 \\  & & & The series splicing & \\
[118] & - & TD-VAR, RMS; & method & 2022 \\  & - & FD-MF, MPF; EMD & Complex vector & \\
[119] & - & FFT & Civil & 2022 \\
[120] & - & SampEn & - & 2012 \\ \hline \hline \end{tabular}  
* The numbers 1–9 in the ‘pre-processing’ column in the table represent amplification, band-pass, low-pass, high-pass, notch filter, rectification, normalization, dimensionality reduction, and other methods, respectively. **Please refer to the Abbreviations part for the explanation of the abbreviations appearing in the Feature Sets and Classifiers columns in the table.  
\end{table} 
Table 2: _Cont._used for classification in [PERSON]'s work, with accuracy rates of 96.16%, 95.07%, and 88.33%, respectively. For the SVM, the RBF core achieves a higher accuracy than the linear method [54]. In [11; 24], the collected sEMG signal was mapped to the control commands using a machine-learning approach based on a Gaussian Process, where a space velocity (three dimensions) and a binary trigger signal were decoded. Then, the LDA classifier was used to interpret the binary trigger signal from the training data. DT, LibSVM, KNN, and NaiveBayes, which are supported by the open-source machine tool Weka, were compared in [40]. For LibSVM, a grid search algorithm tunes the best RBF kernel parameters. The other three classifiers used default parameters. The experimental results show that the four classifiers LibSVM, KNN, DT, and NaiveBayes achieved 96.16%, 94.02%, 76.18%, and 63.87% accuracy, respectively, in the intra-subject test. In a study of sEMG-controlled upper-limb prostheses under force variation conditions, the average error rates of LDA, RF, NaiveBayes, and KNN (k = 3) for hand gesture recognition were 17.42%, 17.97%, 19.07%, and 19.14%, respectively [27]. Long Short-Term Memory (LSTM) has been applied to gait phase recognition in a low-cost system [95]. LSTM has been used to solve the problem of long-term dependence in general RNNs since it was designed. It can effectively transfer and express information in a long time series without causing useful information to be ignored (forgotten). Simultaneously, LSTM can solve the problem of gradient disappearance/explosion in RNNs. The proposed method achieved average classification accuracies of 94.10%, 87.25%, 90.71%, 94.02%, and 87.87%, respectively, for different gaits. In addition, the proposed system exhibits an advantage in terms of real-time performance, with a low average time consumption. Reference [34] worked on daily activity monitoring and fall detection based on sEMG and an accelerometer (ACC).