Бионика интеллекта

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A neural network approach for the auto matic selection of a complex of rehabilitation exercises
(ХНУРЭ, 2021) Butsenko, M. O.; Afanasieva, I. V.; Golian, N. V.; Kameniuk, N.
This article is devoted to solving the problem of automatic selection of a set of rehabilitation exercises during injuries, considering the state of the human cardiovascular system through the use of neural networks. To solve this problem, it was necessary to choose one of two classical approaches – multiclass classification or multilabel classification, each of which solves the problem of data classification through its own algorithm, and use the selected neural network architecture to create a software system. While working on this system, it was also necessary to solve certain problems related to each of these approaches (the need for a large sample due to the large number of exercises that the system should recommend) or a specific approach (inability to select multiple exercises at once – for Multiclass Classification, lower productivity and the number of supported programming languages – for Multilabel Classification). Samples of different sizes (from 1 million records and more) were used to train the neural network, which were generated through a self-written program that generated a given number of records and wrote them to a .CSV (commaseparated values) file.