Since the COVID-19 pandemic, the demand for respiratory rehabilitation has significantly increased. This makes developing home (remote) rehabilitation methods using modern technology essential. New techniques and tools, including wireless sensors and motion capture systems, have been developed to implement remote respiratory rehabilitation. Significant attention during respiratory rehabilitation is paid to the type of human breathing. Remote rehabilitation requires the development of automated methods of breath analysis. Most currently developed methods for analyzing breathing do not work with different types of breathing. These methods are either designed for one type (for example, diaphragmatic) or simply analyze the lungs’ condition. Developing methods of determining the types of human breathing is necessary for conducting remote respiratory rehabilitation efficiently. We propose a method of determining the type of breathing using wireless sensors with the motion capture system. To develop that method, spectral analysis and machine learning methods were used to detect the prevailing spectrum, the marker coordinates, and the prevailing frequency for different types of breathing. An algorithm for determining the type of human breathing is described. It is based on approximating the shape of graphs of distances between markers using sinusoidal waves. Based on the features of the resulting waves, we trained machine learning models to determine the types of breathing. After the first stage of training, we found that the maximum accuracy of machine learning models was below 0.63, which was too low to be reliably used in respiratory rehabilitation. Based on the analysis of the obtained accuracy, the training and running time of the models, and the error function, we choose the strategy of achieving higher accuracy by increasing the training and running time of the model and using a two-stage method, composed of two machine learning models, trained separately. The first model determines whether the breath is of the mixed type; if it does not predict the mixed type of breathing, the second model determines whether breathing is thoracic or abdominal. The highest accuracy achieved by the composite model was 0.81, which surpasses single models and is high enough for use in respiratory rehabilitation. Therefore, using three wireless sensors placed on the patient’s body and a two-stage algorithm using machine learning models, it was possible to determine the type of human breathing with high enough precision to conduct remote respiratory rehabilitation. The developed algorithm can be used in building rehabilitation applications.
The research was carried out on the premises of the experimental study farm of Russian State Agrarian University – Moscow Timiryazev Agricultural Academy in Moscow in 2018–2019. Over two years, the authors collected and microscoped samples of affected plants of different families to determine the causative agents of fungal diseases. The identification of the fungi species of the Alternaria genus was carried out by morphological features of conidia and the habitus of sporulation. It was found that the damage of apple and pear trees is caused by two non-specialized fungus species of Alternaria tenuissima and Alternaria infectoria. It was found that 73% of 110 apple varieties and 47.2% of 53 pear varieties studied were affected to varying degrees by the fungi of the Alternaria genus. The paper describes apple varieties that have been damaged by Alternaria. There has been detected a high malware of Alternaria blight on the young seedlings of pome crops. The species of A. tenuissima and A. infectoria pathogens have been determined to affect dicotyledonous weed plants widely distributed in fruit gardens, as well as a number of garden-protective and ornamental crops. Also, their possible role as infection reservoirs and vectors for fruit crops has been established. The authors considered grade vulnerability to Alternaria blight lesion of different pome and stone fruit varieties. The most Alternaria blight-resistant varieties of pome crops have been identified. The authors have stated the relationship between the degree of Alternaria blight progression and factors such as pest damage and the location of plantings. They also describe some differences in the manifestation of Alternaria blight symptoms on apple and pear trees during the initial period of disease progression. As a result of the studies, recommendations are given on the implementation of protective measures aimed at reducing the spread of Alternaria blight of pome crops in fruit gardens.
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