Image set preparation: A platform to prepare a myoelectric signal to train a CNN
Derived from the good performance in the classification of surface Electromyography signals using CNN for its application in prosthetics, rehabilitation, and medicine, we present a platform that, from a surface Electromyography, performs the necessary digital processing to generate an image database...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | SoftwareX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711023002054 |
| _version_ | 1797679465833168896 |
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| author | Jorge Arturo Sandoval-Espino Alvaro Zamudio-Lara José Antonio Marbán-Salgado J Jesús Escobedo-Alatorre Omar Palillero-Sandoval J. Guadalupe Velásquez Aguilar |
| author_facet | Jorge Arturo Sandoval-Espino Alvaro Zamudio-Lara José Antonio Marbán-Salgado J Jesús Escobedo-Alatorre Omar Palillero-Sandoval J. Guadalupe Velásquez Aguilar |
| author_sort | Jorge Arturo Sandoval-Espino |
| collection | DOAJ |
| description | Derived from the good performance in the classification of surface Electromyography signals using CNN for its application in prosthetics, rehabilitation, and medicine, we present a platform that, from a surface Electromyography, performs the necessary digital processing to generate an image database to train a Convolutional Neural Network. This platform requires inputting the protocol parameters under which the myoelectric signal was acquired. In addition, it allows selection among four groups of Time-Domain features and four types of images that have shown good performance (above 90%) in the current literature. The platform generates images in separate folders for each movement according to the selected parameters. This work offers a valuable tool in classification using surface Electromyography and Convolutional Neural Networks, enabling more efficient customization and optimization of training processes. |
| first_indexed | 2024-03-11T23:15:04Z |
| format | Article |
| id | doaj.art-f8c0799211a248ab8e46bc3ab8da0ba7 |
| institution | Directory Open Access Journal |
| issn | 2352-7110 |
| language | English |
| last_indexed | 2024-03-11T23:15:04Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj.art-f8c0799211a248ab8e46bc3ab8da0ba72023-09-21T04:37:45ZengElsevierSoftwareX2352-71102023-07-0123101509Image set preparation: A platform to prepare a myoelectric signal to train a CNNJorge Arturo Sandoval-Espino0Alvaro Zamudio-Lara1José Antonio Marbán-Salgado2J Jesús Escobedo-Alatorre3Omar Palillero-Sandoval4J. Guadalupe Velásquez Aguilar5Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp), Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, MexicoCentro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp), Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, MexicoCentro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp), Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, Mexico; Corresponding author.Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp), Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, MexicoCentro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp), Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, MexicoFacultad de Ciencias Químicas e Ingeniería (FCQeI), Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, MexicoDerived from the good performance in the classification of surface Electromyography signals using CNN for its application in prosthetics, rehabilitation, and medicine, we present a platform that, from a surface Electromyography, performs the necessary digital processing to generate an image database to train a Convolutional Neural Network. This platform requires inputting the protocol parameters under which the myoelectric signal was acquired. In addition, it allows selection among four groups of Time-Domain features and four types of images that have shown good performance (above 90%) in the current literature. The platform generates images in separate folders for each movement according to the selected parameters. This work offers a valuable tool in classification using surface Electromyography and Convolutional Neural Networks, enabling more efficient customization and optimization of training processes.http://www.sciencedirect.com/science/article/pii/S2352711023002054CNN networkGesture classificationImage processingsEMG |
| spellingShingle | Jorge Arturo Sandoval-Espino Alvaro Zamudio-Lara José Antonio Marbán-Salgado J Jesús Escobedo-Alatorre Omar Palillero-Sandoval J. Guadalupe Velásquez Aguilar Image set preparation: A platform to prepare a myoelectric signal to train a CNN SoftwareX CNN network Gesture classification Image processing sEMG |
| title | Image set preparation: A platform to prepare a myoelectric signal to train a CNN |
| title_full | Image set preparation: A platform to prepare a myoelectric signal to train a CNN |
| title_fullStr | Image set preparation: A platform to prepare a myoelectric signal to train a CNN |
| title_full_unstemmed | Image set preparation: A platform to prepare a myoelectric signal to train a CNN |
| title_short | Image set preparation: A platform to prepare a myoelectric signal to train a CNN |
| title_sort | image set preparation a platform to prepare a myoelectric signal to train a cnn |
| topic | CNN network Gesture classification Image processing sEMG |
| url | http://www.sciencedirect.com/science/article/pii/S2352711023002054 |
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