## 4 Results and Discussion  
After producing a complete dataset, the models were trained for each group of boxers. MLP training took no more than 10 min on a computer with the following parameters: CPU--Intel Core i7; RAM--16 GB; and CPU frequency--3.2 GHz. To assess the effectiveness of the models, the loss and accuracy functions were used. Figures 6-9 present graphs of the loss function and the accuracy function for each group of athletes. On the graphs, \"Train\" is the main dataset, which was used to train the model, and \"Test\" is the test dataset, which was used to validate the model. Figure 6: Graphs of the loss function and accuracy of boxers with \(1\) year of training experience. The analysis of the graphs shows that by the 60 th epoch, the accuracy approaches 1. It is interesting that with the same training parameters among boxers with 1 year of training experience, outliers of the test and basic data divergence are clearly visible on the graphs, and are much greater than on the graphs of more experienced boxers. The graphs show that the network settings are suitable for this type and amount of data. The paper [16] states that \"all models reach a validation score of 1.0 at a training size of approximately 100\". Moreover, in [16], MLP does not show the best dynamics of the learning process. It can be assumed that in [16], there was a significantly smaller dataset, compared to our work. This is important for MLP. In addition, it is not clear from the work [16] what parameters were at input to the MLP. The authors point out that the MLP had three hidden layers of eight nodes, and the measurements were carried out using an IMU that contained an accelerometer, gyroscope, and magnetometer. The input must contain data from three devices, and the punch lasts from 100 to 300 milliseconds. However, in general, we can say that the data obtained by us are comparable with the work [16]. In this series, the analysis of punches and recognition of the type of punches occurred for groups that did not participate in the model training series. No differentiation was  
Figure 8: Graphs of the loss function and accuracy of boxers with more than 5 years of training experience. Figure 7: Graphs of the loss function and accuracy of boxers with 2–3 years of training experience. Figure 9: Graphs of the loss function and accuracy when training a universal ANN. made by the types of punches; the general level of recognition was determined for all punches. The recognition level was calculated as follows:  
\[\text{Recognition level}=\frac{\text{number of correctly recognized punches}}{\text{number of incorrectly recognized punches}}\times 100\%. \tag{2}\]  
In the first group of boxers (1 year of training), recognition averaged 87.2 \(\pm\) 5.4% (see Figure 10, Group model). It was difficult to predict such a situation in advance, but the level of punch recognition according to the universal model turned out to be higher--91.89 \(\pm\) 3.45% (Figure 10, Universal model). The level of recognition is quite high, but from the graph, it is easy to see a large scatter of values, which can be explained by the fact that the technique of punches in this category of athletes does not yet have a constant basis. Figure 11 shows a graph of the experiment of the 2 nd group of boxers. The punch recognition rate is 95.33 \(\pm\) 2.51% (Figure 11, Group model). This is the highest result of all three groups. The recognition of punches according to the universal model did not practically change and amounted to 94.17 \(\pm\) 2.96% (Figure 11, Universal model). Figure 12 shows a graph of the experiment of the 3 rd group of boxers. The punch recognition level is 91.73 \(\pm\) 3.82% (Figure 12, Group model). The lower level of punch recognition and large spread of values can be explained by the fact that high-level athletes have their own peculiar technique of punching. According to the universal model, as well as for group 1, the level of punch recognition was slightly higher--92.93 \(\pm\) 4.33% (Figure 12, Universal model).