Information-extreme machine learning of wrist prosthesis control system based on the sparse training matrix
The article considers the problem of machine learning of a wrist prosthesis control system with a non-invasive biosignal reading system. The task is solved within the framework of information-extreme intelligent data analysis technology, which is based on maximizing the system’s information producti...
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| Format: | Article |
| Language: | English |
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Sumy State University
2022-12-01
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| Series: | Журнал інженерних наук |
| Subjects: | |
| Online Access: | https://jes.sumdu.edu.ua/information-extreme-machine-learning-of-wrist-prosthesis-control-system-based-on-the-sparse-training-matrix/ |
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| author | Suprunenko M. K. Zborshchyk O. P. Sokolov O. |
| author_facet | Suprunenko M. K. Zborshchyk O. P. Sokolov O. |
| author_sort | Suprunenko M. K. |
| collection | DOAJ |
| description | The article considers the problem of machine learning of a wrist prosthesis control system with a non-invasive biosignal reading system. The task is solved within the framework of information-extreme intelligent data analysis technology, which is based on maximizing the system’s information productivity in machine learning. The idea of information-extreme machine learning of the control system for recognition of electromyographic biosignals, as in artificial neural networks, consists in adapting the input information description to the maximum total probability of making correct classification decisions. However, unlike neuro-like structures, the proposed method was developed within a functional approach to modeling the cognitive processes of the natural intelligence of forming and making classification decisions. As a result, the proposed method acquires the properties of adaptability to the intersection of classes in the space of recognition features and flexibility when retraining the system due to the recognition class alphabet expansion. In addition, the decision rules constructed within the framework of the geometric approach are practically invariant to the multidimensionality of the space of recognition features. The difference between the developed method and the well-known methods of information-extreme machine learning is the use of a sparse training matrix, which allows for reducing the degree of intersection of recognition classes significantly. The optimization parameter of the input information description, the training dataset, is the quantization level of electromyographic biosignals. As an optimization criterion is considered the modified Kullback information measure. The proposed machine learning algorithm results are shown in the example of recognition of six finger movements and wrist. |
| format | Article |
| id | doaj-art-18ee4ef35f1e42518317ae9031c9c7c5 |
| institution | DOAJ |
| issn | 2312-2498 2414-9381 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Sumy State University |
| record_format | Article |
| series | Журнал інженерних наук |
| spelling | doaj-art-18ee4ef35f1e42518317ae9031c9c7c52025-08-20T03:17:35ZengSumy State UniversityЖурнал інженерних наук2312-24982414-93812022-12-0192E28E3510.21272/jes.2022.9(2).e4Information-extreme machine learning of wrist prosthesis control system based on the sparse training matrixSuprunenko M. K.0Zborshchyk O. P.1Sokolov O.2Sumy State University, 2, Rymskogo-Korsakova St., 40007 Sumy, UkraineSumy State University, 2, Rymskogo-Korsakova St., 40007 Sumy, UkraineNicolaus Copernicus University in Torun, 5, Grudziadzka St., 87-100 Torun, PolandThe article considers the problem of machine learning of a wrist prosthesis control system with a non-invasive biosignal reading system. The task is solved within the framework of information-extreme intelligent data analysis technology, which is based on maximizing the system’s information productivity in machine learning. The idea of information-extreme machine learning of the control system for recognition of electromyographic biosignals, as in artificial neural networks, consists in adapting the input information description to the maximum total probability of making correct classification decisions. However, unlike neuro-like structures, the proposed method was developed within a functional approach to modeling the cognitive processes of the natural intelligence of forming and making classification decisions. As a result, the proposed method acquires the properties of adaptability to the intersection of classes in the space of recognition features and flexibility when retraining the system due to the recognition class alphabet expansion. In addition, the decision rules constructed within the framework of the geometric approach are practically invariant to the multidimensionality of the space of recognition features. The difference between the developed method and the well-known methods of information-extreme machine learning is the use of a sparse training matrix, which allows for reducing the degree of intersection of recognition classes significantly. The optimization parameter of the input information description, the training dataset, is the quantization level of electromyographic biosignals. As an optimization criterion is considered the modified Kullback information measure. The proposed machine learning algorithm results are shown in the example of recognition of six finger movements and wrist.https://jes.sumdu.edu.ua/information-extreme-machine-learning-of-wrist-prosthesis-control-system-based-on-the-sparse-training-matrix/information-extreme intelligent technologymachine learningprocess innovationsparse training matrixprosthesis control systeminformation criterionelectromyographic sensorbiosignal |
| spellingShingle | Suprunenko M. K. Zborshchyk O. P. Sokolov O. Information-extreme machine learning of wrist prosthesis control system based on the sparse training matrix Журнал інженерних наук information-extreme intelligent technology machine learning process innovation sparse training matrix prosthesis control system information criterion electromyographic sensor biosignal |
| title | Information-extreme machine learning of wrist prosthesis control system based on the sparse training matrix |
| title_full | Information-extreme machine learning of wrist prosthesis control system based on the sparse training matrix |
| title_fullStr | Information-extreme machine learning of wrist prosthesis control system based on the sparse training matrix |
| title_full_unstemmed | Information-extreme machine learning of wrist prosthesis control system based on the sparse training matrix |
| title_short | Information-extreme machine learning of wrist prosthesis control system based on the sparse training matrix |
| title_sort | information extreme machine learning of wrist prosthesis control system based on the sparse training matrix |
| topic | information-extreme intelligent technology machine learning process innovation sparse training matrix prosthesis control system information criterion electromyographic sensor biosignal |
| url | https://jes.sumdu.edu.ua/information-extreme-machine-learning-of-wrist-prosthesis-control-system-based-on-the-sparse-training-matrix/ |
| work_keys_str_mv | AT suprunenkomk informationextrememachinelearningofwristprosthesiscontrolsystembasedonthesparsetrainingmatrix AT zborshchykop informationextrememachinelearningofwristprosthesiscontrolsystembasedonthesparsetrainingmatrix AT sokolovo informationextrememachinelearningofwristprosthesiscontrolsystembasedonthesparsetrainingmatrix |