## 4 Results  
Each contraction of the arm muscles during one of the seven movements was recorded by 8 EMG sensors. Estimates of temporal signals were calculated from the recorded signals. The average values of the statistical indicators of the eight sensors are presented in Figure 2. The minimum standard deviation of 0.007 was obtained when measuring the 6 th gesture. The highest standard deviation of 0.253 was recorded for the 7 th gesture. The minimum value \(-\)0.77 was obtained when measuring the 2 nd gesture. The highest min value of \(-\)0.027 was recorded during the 6 th gesture. The minimum (max) value was 0.007 when measuring the 6 th gesture. The maximum (max) value of 0.77 was recorded during the 2 nd gesture. The minimum value of 5.87 was obtained when measuring the 6 th gesture. The highest value of 90.87 was recorded during the 7 th gesture. For the average sensor, the minimum amplitude change value of 0.02 was obtained when measuring the 6 th gesture. The maximum amplitude change value of 0.735 was recorded during the 7 th gesture. The minimum value of the first amplitude jump of 0.018 was obtained when measuring the 7 th gesture. The highest value of the first amplitude jump of 0.447 was recorded during the 3 rd gesture. The minimum absolute value of 0.005 was obtained when measuring the 6 th gesture. The highest absolute value of 0.189 was recorded for the 7 th gesture. The minimum wavelength value of 1.19 was obtained during the measurement of the 6 th gesture. The highest wavelength value of 46.26 was recorded during the 7 th gesture. Minimum Wilson amplitude value of 0.0 was obtained by measuring the 1 st, 2 nd, 5 th, and 6 th gestures. The highest [PERSON] amplitude value of 10 was recorded during the 7 th gesture. The minimum, mean, and maximum values of the gesture features are summarized in Tables 1-3, respectively. Figure 2: Summary of EMG signal values for gestures 1–7. PCA showed that gesture classes overlap strongly in the feature space. There are no clear dividing surfaces between classes to identify a separate group of gestures. In order to solve the motion classification of gestures, it is necessary to apply non-linear classification methods, which can be used to evaluate the non-linear distribution of the data. For this purpose, the random forest classifier is used in the project research. Figure 3 shows the projections of the EMG signals to the two principle components P1 and P2. Different classes of gestures are represented by different colours. Each individual point represents one measurement of hand motion in the two-dimensional space of P1, and P2. Each individual movement measurement is represented by a separate circle, the colour of which depends on the gesture class (type). From the cloud of points, it can be seen that the different classes of gestures overlap strongly, and no clear dividing line can be drawn between them. \begin{table}
\begin{tabular}{p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt}} \hline \hline
**Gesture** & \begin{tabular}{} \end{tabular} & **Minimum** & **Maximum** & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
1 & 0.055 & \(-\)0.229 & 0.225 & 49.91 & 0.141 & 0.099 & 0.036 & 8.87 & 2.878 \\ \hline
2 & 0.089 & \(-\)0.333 & 0.317 & 51.13 & 0.223 & 0.117 & 0.059 & 14.59 & 3.66 \\ \hline
3 & 0.101 & \(-\)0.357 & 0.32 & 58.91 & 0.259 & 0.190 & 0.066 & 16.2 & 4.22 \\ \hline
4 & 0.081 & \(-\)0.286 & 0.276 & 52.53 & 0.16367 & 0.061 & 0.054 & 13.43 & 3.49 \\ \hline
5 & 0.058 & \(-\)0.234 & 0.212 & 48.64 & 0.069 & 0.098 & 0.040 & 8.976 & 2.746 \\ \hline
6 & 0.014 & \(-\)0.065 & 0.049 & 22.85 & 0.17433 & 0.05 & 0.009 & 2.299 & 0.773 \\ \hline
7 & 0.143 & \(-\)0.434 & 0.415 & 73.46 & 0.411 & 0.297 & 0.104 & 25.58 & 5.928 \\ \hline \hline \end{tabular}
\end{table}