Identification of hand gestures using pattern recognition of electromyography signals acquired with MyoArmband
The most used way to record hand gestures' information is through the Electromyography (EMG) technique. However, the research in the area is still fragmented. This study aimed at reproducing the high classification performance of hand gestures using EMG data reported in the literature, using a...
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| Principais autores: | , |
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| Formato: | Online |
| Idioma: | português |
| Publicado em: |
Universidade Estadual de Campinas
2019
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| Acesso em linha: | https://econtents.sbu.unicamp.br/eventos/index.php/pibic/article/view/2283 |
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| _version_ | 1864359034229882880 |
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| author | Sgambato, Bruno Castellano, Gabriela |
| author_facet | Sgambato, Bruno Castellano, Gabriela |
| author_sort | Sgambato, Bruno |
| collection | Portal de Eventos Científicos da UNICAMP |
| container_reference | Revista dos Trabalhos de Iniciação Científica da UNICAMP; n. 27 (2019): Congresso de Iniciação Científica UNICAMP; 1-1 |
| description | The most used way to record hand gestures' information is through the Electromyography (EMG) technique. However, the research in the area is still fragmented. This study aimed at reproducing the high classification performance of hand gestures using EMG data reported in the literature, using a MyoArmband EMG equipment for data acquisition and an LDA classifier, and testing different features and feature selection techniques. The results showed that a performance of 75% is achievable with selected features. |
| first_indexed | 2025-10-08T13:56:58Z |
| format | Online |
| id | ojs-article-2283 |
| institution | Universidade Estadual de Campinas |
| issn | 2596-1969 |
| language | por |
| last_indexed | 2025-10-08T13:56:58Z |
| publishDate | 2019 |
| publisher | Universidade Estadual de Campinas |
| record_format | ojs |
| spelling | ojs-article-22832025-09-26T14:39:16Z Identification of hand gestures using pattern recognition of electromyography signals acquired with MyoArmband Sgambato, Bruno Castellano, Gabriela Electromyography Classification Hand gestures The most used way to record hand gestures' information is through the Electromyography (EMG) technique. However, the research in the area is still fragmented. This study aimed at reproducing the high classification performance of hand gestures using EMG data reported in the literature, using a MyoArmband EMG equipment for data acquisition and an LDA classifier, and testing different features and feature selection techniques. The results showed that a performance of 75% is achievable with selected features. Universidade Estadual de Campinas 2019-01-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Artigo de convidado application/pdf https://econtents.sbu.unicamp.br/eventos/index.php/pibic/article/view/2283 10.20396/revpibic2720192283 Revista dos Trabalhos de Iniciação Científica da UNICAMP; n. 27 (2019): Congresso de Iniciação Científica UNICAMP; 1-1 2596-1969 por https://econtents.sbu.unicamp.br/eventos/index.php/pibic/article/view/2283/2333 Copyright (c) 2019 Revista dos Trabalhos de Iniciação Científica da UNICAMP http://creativecommons.org/licenses/by/4.0 |
| spellingShingle | Sgambato, Bruno Castellano, Gabriela Electromyography Classification Hand gestures Identification of hand gestures using pattern recognition of electromyography signals acquired with MyoArmband |
| title | Identification of hand gestures using pattern recognition of electromyography signals acquired with MyoArmband |
| title_full | Identification of hand gestures using pattern recognition of electromyography signals acquired with MyoArmband |
| title_fullStr | Identification of hand gestures using pattern recognition of electromyography signals acquired with MyoArmband |
| title_full_unstemmed | Identification of hand gestures using pattern recognition of electromyography signals acquired with MyoArmband |
| title_short | Identification of hand gestures using pattern recognition of electromyography signals acquired with MyoArmband |
| title_sort | identification of hand gestures using pattern recognition of electromyography signals acquired with myoarmband |
| topic | Electromyography Classification Hand gestures |
| url | https://econtents.sbu.unicamp.br/eventos/index.php/pibic/article/view/2283 |
| work_keys_str_mv | AT sgambatobruno identificationofhandgesturesusingpatternrecognitionofelectromyographysignalsacquiredwithmyoarmband AT castellanogabriela identificationofhandgesturesusingpatternrecognitionofelectromyographysignalsacquiredwithmyoarmband |
