## Appendix A Selected Features  
The equations are considered on a data window of length \(L\), where the \(k\)-th element of the \(i\)-th window corresponds to \(x_{i,k}\).  
1. **Mean absolute value (MAV)**. This is the mean of the absolute value of the sEMG signal (fully rectified signal). This descriptor is commonly used for EMG-based monitoring applications as it is capable of detecting muscle contraction levels. \[MAV(x_{i})=\frac{1}{L}\sum_{k=0}^{L-1}\left|x_{i,k}\right|\] (1)
2. **Standard deviation (SD)**. This measures the amount of variation in the EMG signal. \[SD(x_{i})=\sqrt{\frac{1}{L}\sum_{k=0}^{L-1}\left|x_{i,k}-\mu_{i}\right|^{2}}\] (2) Here, \(\mu_{i}\) is the mean of the windowed signal: \[\mu_{i}=\frac{1}{L}\sum_{k=0}^{L-1}x_{i,k}\] (3)
3. **Variance (VAR)**. This is a power index of the EMG signal. Variance is defined as an average of square values of the deviation: \[VAR(x_{i})=\frac{1}{L}\sum_{k=0}^{L-1}x_{i,k}^{2}\] (4)
4. **Waveform Length (WL)**. This parameter provides a straightforward characterization of the waveform, as it represents the cumulative length of the EMG signal within a specified time window. \[WL(x_{i})=\sum_{k=1}^{L-1}\left|x_{i,k}-x_{i,k-1}\right|\] (5)
5. **Root Mean Square (RMS)**. This parameter is strongly linked to the standard deviation, as they are identical when the signal's mean value is zero. It is determined using the following equation: \[RMS(x_{i})=\sqrt{\frac{1}{L}\sum_{k=0}^{L-1}x_{i,k}^{2}}\] (6)
6. **Integrated EMG (IEMG)**. This is the sum of the fully rectified signal and is related to the EMG signal sequence firing point. \[IEMG(x_{i})=\sum_{k=0}^{L-1}\left|x_{i,k}\right|\] (7)
7. **Zero Crossing (ZC)**. This metric represents the frequency at which the signal crosses zero, essentially counting how many times the signal intersects the y-axis. The datacaptured by the Myo armband includes values ranging from \(-128\) to \(128\), centered around zero.  
\[ZC(x_{i})=\sum_{k=1}^{L-1}\left(((x_{i,k}-x_{i,k-1})\geq\epsilon)\wedge(sgn(x_{ i,k})\
eq sgn(x_{i,k-1}))\right) \tag{10}\]  
Here, \(\epsilon\) is a threshold parameter, and Boolean operations like \(\geq\) or \(\
eq\) return 1 when TRUE and 0 when FALSE.  
8. **Slope sign changes (SSCs).** This parameter quantifies the frequency of slope sign changes in the signal. It calculates the number of transitions between positive and negative slopes over three consecutive samples, incorporating a threshold parameter (\(\epsilon\)) to minimize the impact of noise in the sEMG signal.  
\[SSC(x_{i})=\sum_{k=1}^{L-1}f[(x_{i,k}-x_{i,k-1})\cdot(x_{i,k}-x_{i,k+1})],\ \ \text{with}\ f(x)=\begin{cases}1,\ \ if\ x\geq\epsilon\\ 0,\ otherwise\end{cases} \tag{11}\]  
The next features are frequency domain features, meaning that the windowed signal must be first transformed with a Fourier transformation. In these expressions, \(f_{i}\) is the frequency of the spectrum in the bin \(i\) and \(P_{i}\) is the power spectrum at frequency bin \(i\).  
9. **Median Frequency (MF).** This is the average frequency of the signal.  
\[MF=\frac{\sum_{i=0}^{N-1}f_{i}P_{i}}{\sum_{i=0}^{N-1}P_{i}} \tag{12}\]  
10. **Mean Power Frequency (MPF).** This is the average of the power spectrum of the signal.  
\[MF=\frac{\sum_{i=0}^{N-1}P_{i}}{\text{N}} \tag{13}\]