Development of a method for determining the dominant type of human breathing pattern based on computer vision technologies, motion capture systems and machine learning

The study raises the problem of the absence of methods for determining the dominant type of breathing pattern that can be used in the implementation of software products that contribute to the support of patients with respiratory insufficiency and their rehabilitation at the stage of inpatient and outpatient treatment. Existing methods are either too labor-intensive to implement due to the excessive amount of markers utilized by motion capture systems or economically unprofitable due to the cost of the equipment itself or developed only for research purposes and are not applicable in clinical practice. In this regard, this article is aimed at developing a method for determining the type of breathing, which could later be employed for automated rehabilitation of patients with respiratory insufficiency. As part of the study, computer vision and machine learning methods were applied as well as methods based on motion capture technologies. The article presents methods for determining the position of markers in space and analyzing the type of human breathing (thoracic, abdominal, mixed) in real time based on the data obtained by means of motion capture system markers. The materials of the article are of practical value in the field of medical rehabilitation of patients with respiratory insufficiency; they make it possible to optimize labor processes within the field of medical rehabilitation, i.e. reducing labor and time costs of rehabilitologists.

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