Abstract
In this work we applied real-time classification of prosthetic fingers movements using surface electromyography (sEMG) data. We employed support vector machine (SVM) for classification of fingers movements. SVM has some benefits over other classification techniques e.g. 1) it avoids overfitting, 2) handles nonlinear data efficiently and 3) it is stable. SVM is employed on Raspberry pi which is a low-cost, credit-card sized computer with high processing power. Moreover, it supports Python which makes it easy to build projects and it has multiple interfaces available. In this paper, our aim is to perform classification of prosthetic hand relative to human fingers. To assess the performance of our framework we tested it on ten healthy subjects. Our framework was able to achieve mean classification accuracy of 78%.
Originalsprog | Engelsk |
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Titel | BIBE 2021 - 21st IEEE International Conference on BioInformatics and BioEngineering, Proceedings |
Forlag | IEEE |
Publikationsdato | 2021 |
Artikelnummer | 9635461 |
ISBN (Trykt) | 978-1-6654-4262-6 |
ISBN (Elektronisk) | 978-1-6654-4261-9 |
DOI | |
Status | Udgivet - 2021 |
Begivenhed | 21st IEEE International Conference on BioInformatics and BioEngineering, BIBE 2021 - Kragujevac, Serbien Varighed: 25 okt. 2021 → 27 okt. 2021 |
Konference
Konference | 21st IEEE International Conference on BioInformatics and BioEngineering, BIBE 2021 |
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Land/Område | Serbien |
By | Kragujevac |
Periode | 25/10/2021 → 27/10/2021 |
Sponsor | Institute of Electrical and Electronic Engineers (IEEE), Ministry of Education, Science and Technological Development of Republic Of Serbia, Research and Development Center for Bioengineering BioIRC, University of Kragujevac |
Navn | International Conference on Bioinformatics and Bioengineering |
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ISSN | 2471-7819 |
Bibliografisk note
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