## 2 Equipment and Methods of Capturing and Preparing Data
### Signal Spectrum Analysis  
The accuracy of the classical ML models, when trained on kinematic data, was not good. This can be explained by the lack of training data. Since experimentation is a lengthy process, the researchers tried to approach this problem from the side of classical signal processing problems and began to analyze the spectral characteristics obtained from existing data [29; 30]. We conducted the study using the software preprocessor for forming and solving the nonlinear dynamics equations from the FRUND package [31], which was developed at Volgograd State Technical University. The example of transforming the coordinates of the autospectrum power for one complete breathing cycle is presented in Figure 4.  
Figure 4: An example of transforming the coordinates of one marker in one respiratory cycle. The analysis of the autospectrum power signal revealed the patterns shown in Figure 5.  
As the diagrams show, most of the participants demonstrated a significant predominance of changes in the Y coordinate of the first (thoracic) marker during thoracic breathing; also, changes in the Z coordinates of different markers were also predominant. For thoracic breathing, the frequency ranges of the predominant spectra were divided according to Figure 6. Most of the spectral peaks were in either the 0.15-0.17 Hz or 0.19-0.21 Hz ranges. The spectral analysis of abdominal breathing revealed the following patterns: patterns for the predominant spectra are presented in Figure 7; changes in the X coordinates of the third (abdominal) and the first markers were predominant for most of the participants. For abdominal breathing, the frequency ranges of the predominant spectra were divided as shown in Figure 8. All values fall within the range of values presented in the figure with an accuracy of 0.05 Hz. According to this diagram, there is an intersection of groups of frequencies of abdominal breathing with groups of thoracic breathing. Therefore, it is impossible to reliably discern the type of breathing by the frequency group. Figure 5: Distribution of characteristic spectra during thoracic breathing by coordinates and markers. Figure 6: Frequency distribution of spectral peaks during thoracic breathing. For mixed (diaphragmatic-costal) breathing, only changes in the coordinates of the first (thoracic) marker were predominant, mostly the Y coordinate. The correlation of the predominant spectra is presented in Figure 9.  
Figure 8: Frequency distribution of spectral peaks during abdominal breathing. Figure 7: Distribution of characteristic spectra during abdominal breathing by coordinates and markers. Figure 4:0 Distribution of characteristic spectra during chest-abdominal breathing by coordinates and markers. When analyzing frequencies, there is also an intersection with other types of breathing in frequency groups. This indicates the impossibility of using frequency groups as the only criterion for determining the type of breathing. The correlation of the predominant frequencies in chest-abdominal breathing is presented in Figure 4:1  
Figure 11 shows an example of the intersection of the predominant spectra for different types of breathing. This example is relatively good for determining the breathing type because all three types of breathing can be distinguished on this graph. Even though the common predominant spectra can be determined by the type of marker coordinate change, one can distinguish types of breathing by frequency groups. However, as mentioned earlier, the frequency groups intersect as do the groups of changes in the predominant coordinates of the markers. Therefore, it is impossible to take the combination of the predominant frequency and the predominant spectrum as an unambiguous criterion for determining the type of breathing. Figure 4:2 Overlay of spectral peaks for different types of breathing. Figure 4:3 Frequency distribution of spectral peaks during chest-abdominal breathing.