# Review of sEMG for Robot Control: Techniques and Applications
## 3 Processing and Pattern Recognition Methods
### Pre-Processing
Thus, the strength difference between muscles (including individual differences) is inconvenient when comparing the effort levels during muscle contraction. In this case, normalization is beneficial. In addition to the above pre-processing methods, there are rectification, dimension reduction, and other methods. The pre-processing techniques used in related studies are summarized in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Pre-Processing * & **Feature Sets ** & **Classifiers ** & **Publish Time** \\ \hline
[4] & 1-2-5 & RMS, MAV & SVR & 2014 \\  & & logarithmic transferred time-domain & LDA, KNN, & \\
[6] & 2 & features, & NaiveBayes & 2017 \\  & & traditional time-domain features & & \\
[7] & 1-2 & fApEn & - & 2014 \\
[11] & - & WL, SSC, ZCR, AMP & Gauss Process & 2021 \\
[14] & 2 & RMS, MFR, MPF & - & 2014 \\
[18] & - & STFT, WPT & - & 2014 \\
[22] & - & RMS & Fuzzy, NN & 2012 \\
[24] & - & AMP, SSC, ZCR, WL & LDA & 2018 \\
[26] & 2-5 & MPF, MDF & - & 2018 \\
[27] & - & self-defined features & LDA, NaiveBayes, RF, & 2016 \\  & & KNN & & \\
[32] & 2-3-6 & & MAV, VAR, WL, HIST, & \\  & & CC, mDWT & MLP(ANN), SVM & 2015 \\
[37] & - & AMP, SSC, ZCR, WL & Gauss Process & 2018 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of pre-processing, feature sets, and classifiers. \begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Pre-Processing *** & **Feature Sets ** & **Classifiers ** & **Publish Time** \\ \hline
[38] & 1-2 & MAV, AR & Improved dynamic & \\  & & MAV, AR & time-warping & 2014 \\  & & & & algorithm & \\  & & MAV, Reflection Coefficients, & \\  & & Histogram, RMS, & & \\
[40] & 2 & Autoregressive coefficient, Variance, & & \\  & & Williamson amplitude, Modified Median & & \\  & & Frequency, & & \\  & & Modified Mean Frequency & & \\  & & 1-Time Domain & & \\  & & 2-Enhanced Time Domain & SVM, ANN, RF, KNN, & \\
[43] & - & 3-NinaPro Features & LDA & 2019 \\  & & 4-SampEn, CC, RMS, WL & & \\
[44] & 8 & IEMG, MAV, SSI, RMS, LOG, VAR & KNN & 2018 \\
[46] & 2 & MAV, VAR, SSC, WL, MNF & Linear SVM & 2021 \\
[49] & - & MAV, ZC, SSC, WL, AR & LDA & 2022 \\
[50] & 9 & RMS, VAR, MAV, SD & ELM & 2019 \\
[51] & 2 & RMS, AR, WL, ZC & Linear Bayes & 2017 \\
[52] & 1-2-3-5 & RMS, WL, ZC, MNF, AR & SVM & 2020 \\
[54] & - & MAV, WL, RMS, AR, ZC, SSC & SVM, LDA, KNN & 2019 \\
[55] & - & RMS, MMAV, MA/MTP, MPF & SVM & 2020 \\
[57] & 2-5 & MAV, WL, ZC, SSC & SVM & 2020 \\
[59] & - & Spatiotemporal characteristics & MEMD, CRNN & 2020 \\
[60] & 5-9 & RMS & SVM & 2008 \\
[62] & 2 & VAR, ZC, iEMG, WAMP & - & 2020 \\
[64] & - & RMS & GRU-RNN & 2021 \\
[67] & 3-6 & - & ConvEMG, LSTM & 2021 \\
[68] & 2 & TDD-FT, SSD & - & 2022 \\
[69] & 3 & - & RF, SVM & 2021 \\
[70] & 12 & WPT & LDB & 2007 \\
[74] & 9 & MAV, ZC, SSC, WL, RMS & SVM & 2020 \\
[75] & 5-9 & WPT & SVM, BPNN & 2020 \\  & & RMS, bursting initial time, bursting & & \\
[76] & 3-5-8-9 & duration, bursting area, and the & - & 2013 \\  & & maximum value & & \\
[80] & 3 & RMS, Autocovariance function & NN, Fuzzy & 2009 \\
[81] & 3-4-6-7 & IAV, WL & - & 2011 \\
[82] & 3-6-7 & IAV, ZC, VAR, MDF & - & 2010 \\
[87] & - & RMS & Fuzzy, NN & 2007 \\
[93] & - & MAV & Fuzzy, NN & 2004 \\
[94] & 2 & STFT & - & 2020 \\
[95] & - & RMS, iEMG & LSTM, MLP & 2020 \\
[96] & 2 & RMS, WL, VR & - & 2021 \\
[97] & - & RMS, WL, SSI, MAV, VAR, LOG, & ESECW & 2021 \\  & & SampEn & & \\
[98] & 5-6-9 & OMA self-defined features & ANN & 2017 \\
[99] & - & WPT-PCA & SUKF, NN & 2020 \\
[112] & 2-5-9 & - & Linear regression, SVM & 2010 \\
[113] & 9 & AMP, SSC, ZCR, WL & - & 2020 \\
[114] & - & iEMG, MAV, MMAV, VAR, WL, & KNN & 2012 \\  & & WAMP & & \\  & & RMS, WL, ZC, IAV, SSC, & & \\
[115] & 1-3-4-5 & RMS, WL, ZC, IAV, SSC, & & \\  & & AR & & \\
[116] & - & SampEn, CC, RMS, WL & - & 2013 \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._