Selection of sEMG-based Configuration for a Hand Gesture Recognition System

UDC.coleccionInvestigaciónes_ES
UDC.conferenceTitleEUSIPCO 2024: 32nd European Signal Processing Conferencees_ES
UDC.departamentoEnxeñaría de Computadoreses_ES
UDC.endPage1645es_ES
UDC.grupoInvGrupo de Tecnoloxía Electrónica e Comunicacións (GTEC)es_ES
UDC.journalTitleProceeeing of 32nd European Signal Processing Conference EUSIPCO 2024es_ES
UDC.startPage1641es_ES
dc.contributor.authorVázquez Araújo, Francisco Javier
dc.contributor.authorDapena, Adriana
dc.contributor.authorLaport, Francisco
dc.contributor.authorCastro-Castro, Paula-María
dc.contributor.authorIglesia, Daniel I.
dc.date.accessioned2024-09-19T13:07:05Z
dc.date.available2024-09-19T13:07:05Z
dc.date.issued2024-08
dc.description.abstract[Abstract]: In recent decades, extensive research has been conducted on the analysis of Electromyography (EMG) signals, aiming to establish a novel communication pathway that utilizes the electrical activity generated by muscle contractions to control external devices. However, determining the optimal configuration for such systems in a given scenario remains as a challenging task. The challenges arise from two main factors: the growing number of available feature extraction methods and classification algorithms, and the necessity of designing control systems that prioritize user comfort, with considerations such as a reduced number of electrodes and fast reaction times. In this paper we propose a method to determine the most suitable configuration for an EMG system by considering three crucial parameters in control systems: reaction time, accuracy, and the required number of channels.es_ES
dc.description.sponsorshipXunta de Galicia (grants ED431C 2020/15 and ED481B 2022/012), MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR (grant TED2021-130240B-I00 (IVRY)). CITIC, as a center accredited for excellence within the Galician University System and a member of the CIGUS Network, receives subsidies from the Department of Education, Science, Universities, and Vocational Training of the Xunta de Galicia. Additionally, it is co-financed by the EU through the FEDER Galicia 2021-27 operational program (Ref. ED431G 2023/01).es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2020/15es_ES
dc.description.sponsorshipXunta de Galicia; ED481B 2022/012es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2023/01es_ES
dc.identifier.citationVázquez-Araujo, F. J., Dapena, A., Laport F. et al. Selection of sEMG-based Configuration for a Hand Gesture Recognition System. Proceeeing of 32nd European Signal Processing Conference EUSIPCO 2024, 2024, 1641-1645. https://eurasip.org/Proceedings/Eusipco/Eusipco2024/pdfs/0001641.pdfes_ES
dc.identifier.isbn978-9-4645-9361-7
dc.identifier.urihttp://hdl.handle.net/2183/39128
dc.language.isoenges_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-130240B-I00/ES/DETECCIÓN INTEGRADA DE VÍDEO Y RADAR PARA EL POSICIONAMIENTO EN INTERIORES DE PERSONAS SIN DISPOSITIVOS Y CON GARANTÍA DE PRIVACIDAD BASADA EN edge AI.es_ES
dc.relation.urihttps://eurasip.org/Proceedings/Eusipco/Eusipco2024/pdfs/0001641.pdfes_ES
dc.rights.accessRightsopen accesses_ES
dc.subjectControl systemses_ES
dc.subjectElectromyographyes_ES
dc.subjectHand gestureses_ES
dc.titleSelection of sEMG-based Configuration for a Hand Gesture Recognition Systemes_ES
dc.typeconference outputes_ES
dspace.entity.typePublication
relation.isAuthorOfPublication48992e38-2103-4f34-b0c2-c4e8fbd2a2e4
relation.isAuthorOfPublication91c5c67f-2bb0-4420-92ec-457806e8cf96
relation.isAuthorOfPublication53b7aaca-4173-401b-94f9-37275a0a17b4
relation.isAuthorOfPublication6d98941b-5537-49e3-84aa-16b84949f66d
relation.isAuthorOfPublicationf91c8572-76d0-48af-a714-c679bb6e1fb4
relation.isAuthorOfPublication.latestForDiscovery48992e38-2103-4f34-b0c2-c4e8fbd2a2e4

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
VazquezAraujo_Francisco_2024_Selection_sEMG-based_Configu_Gesture_Recog_System.pdf
Size:
676.49 KB
Format:
Adobe Portable Document Format
Description:
Accepted Manuscript