## 5 Conclusions  
Experiments have shown that the use of ANN in the form of MLP greatly facilitates the collection of data on the kinetic characteristics of boxers' punches, allowing this process to be automated. Parameters of punches, such as the speed and acceleration, can be determined during shadow boxing, while the type of punch and its parameters can be identified. It is known that the characteristics of a punch in a fight and when a boxer strikes outside of a fight are different. It is not difficult to measure the parameters of a punch during a fight with the modern development of technology, but to identify punches, one needs to specially conduct video capture of movements, and this is a very time-consuming and expensive procedure, which is practically very difficult to carry out for several dozen boxers. However, in the future, with the further development of technologies, video capture may take a leading role in recognizing movements in martial sports.  
Machine learning often uses a data augmentation strategy that allows one to significantly increase the diversity of data available for training models. In this work, the method of \"repetition without repetition\" was used; that is, boxers were instructed to apply each punch differently, without changing the type of punch. At the same time, data augmentation was applied, which increased the efficiency of the developed model.  
Separate models were developed based on data for specific groups of boxers, divided by the level of training. A universal model was also developed, based on data  
Figure 12: Graphs showing the level of recognition of punches of a group of advanced boxers (more than 5 years of training).  
from all groups. At the same time, the use of the universal model showed a better classification accuracy.  
This makes it possible to put forward the hypothesis that it is possible to develop an ideal model of a boxing punch, based on the data of highly qualified boxers. Furthermore, the level of deviation from this model will correlate with the level of development of the athlete's boxing technique.  
This hypothesis is supported by the fact that the worst results in terms of the punch recognition accuracy were shown by the group of boxers with the initial level of training (for the universal model, 91.89 \(\pm\) 3.45%) and the group of high-level boxers (for the universal model, 92.93 \(\pm\) 4.33%). It can be concluded that this is due to the fact that these boxers' punches differ too much in technique.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee at Financial University under the Government of the Russian Federation (20200901a).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data available on request due to privacy restrictions.  
The author would like to thank athletes, coaches, and the leadership of the Boxing Federation of the Republic of Tatarstan (Kazan, Russia) for organizing the experiments and actively participating in the study.  
The authors declare no conflict of interest.