###### Abstract
In modern information technology, wireless sensors, wireless sensor networks (WSN), motion capture systems, crowdsensing, virtual reality technologies, etc. are often used to improve productivity, efficiency, and security [5; 6; 7] when solving various problems in different areas: e.g., in ecology [7], the early detection of fires and landslides became possible with the help of WSNs; they also help monitor air quality. In military applications, WSNs track the enemy actions and help make decisions about the further movement of armed forces [8]. Medicine is no exception. Sensors and WSNs are used to improve patient care in rehabilitation and diagnostic processes, including respiratory rehabilitation [9; 10]. This article is devoted to breathing analysis to provide biofeedback through interaction with the interactive gamified environment. Many research teams in the scientific community are engaged in studying issues of breath analysis [10; 11; 12; 13; 14]. For example, [PERSON] et al. [10] study the possibilities of using human respiratory sounds as diagnostic markers of COVID-19 in uncontrolled crowdsourced data. Machine learning and shallow classifiers, such as logistic regression (LR), gradient boosting trees, and support vector machines (SVMs), were used for data analysis in that work. The results show that performance for all tasks exhibited above an 80% area under curve (AUC) score. [PERSON] et al. [15] present a noncontact method of studying the respiratory health of patients with COVID-19 using RGB infrared sensors to analyze their breathing patterns. The authors were able to determine the state of health of the respiratory organs (i.e., whether the respiratory organs were healthy or not) with an accuracy of 70.83% by processing the obtained data using neural networks such as LSTM, GRU, and BiG RU. [PERSON] et al. studied chronic lung disease and presented a diaphragmatic breathing monitoring system with a high accuracy (95.5%) and stable operation in various environments. However, the system tracks only one type of breathing and is poorly suited for respiratory failure recovery [16]. [PERSON] presents a mobile application for measuring lung functions [17]. That system differs from the others in its cheapness because it only requires an Android mobile phone. However, the sample used in the study was relatively small, and it focused on determining only one type of breathing. Breath analysis for the diagnosis of various diseases was a popular topic even before the COVID-19 epidemic. [PERSON] et al. [18] present a noncontact method of real-time breath analysis implemented in Python which was quite accurate. The model was able to determine 9.57 out of 10 breaths taken per minute. Respiration parameters were extracted from thermal data in real time. The Table 1 presents a comparative analysis of the presented methods. These works show that the analysis of the respiratory organs and the breath itself is a popular way to diagnose various diseases, including COVID-19. This method can provide significant assistance to doctors in diagnosing diseases in the near future. However, the problem of the classification of respiration remains unsolved, even though it is essential for automatizing respiratory rehabilitation. In this study, we aimed to answer the following research questions:  
* **RQ1**. Can kinematic data taken from key points of the torso be used to determine the person's characteristic type of breathing with satisfactory accuracy for respiratory rehabilitation? * **RQ2**. What is the difference in frequency responses of the person's torso's marker movement for different types of breathing? The rest of the article is organized as follows. In Section 2, we discuss the equipment and methods we used to capture the breathing data, preprocess it, and describe the results of preliminary spectrum analysis of the data on different types of breathing. In Section 3, we show the results of training different machine learning models on this data and discuss their accuracy.