sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control
Ver/ Abrir
Impacto
Scholar |
Otros documentos de la autoría: Mora, Marta Covadonga; García-Ortiz, José V.; Cerdá Boluda, Joaquín
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7035
comunitat-uji-handle3:10234/8617
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand ControlFecha de publicación
2024Editor
MDPIISSN
1424-8220Cita bibliográfica
MORA, Marta C.; GARCÍA-ORTIZ, José V.; CERDÁ-BOLUDA, Joaquín. sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control. Sensors, 2024, vol. 24, núm. 7, p. 2063Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.mdpi.com/1424-8220/24/7/2063Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
The design and control of artificial hands remains a challenge in engineering. Popular prostheses are bio-mechanically simple with restricted manipulation capabilities, as advanced devices are pricy or abandoned due ... [+]
The design and control of artificial hands remains a challenge in engineering. Popular prostheses are bio-mechanically simple with restricted manipulation capabilities, as advanced devices are pricy or abandoned due to their difficult communication with the hand. For social robots, the interpretation of human intention is key for their integration in daily life. This can be achieved with machine learning (ML) algorithms, which are barely used for grasping posture recognition. This work proposes an ML approach to recognize nine hand postures, representing 90% of the activities of daily living in real time using an sEMG human–robot interface (HRI). Data from 20 subjects wearing a Myo armband (8 sEMG signals) were gathered from the NinaPro DS5 and from experimental tests with the YCB Object Set, and they were used jointly in the development of a simple multi-layer perceptron in MATLAB, with a global percentage success of 73% using only two features. GPU-based implementations were run to select the best architecture, with generalization capabilities, robustness-versus-electrode shift, low memory expense, and real-time performance. This architecture enables the implementation of grasping posture recognition in low-cost devices, aimed at the development of affordable functional prostheses and HRI for social robots. [-]
Publicado en
Sensors, 2024, vol. 24, núm. 7, p. 2063Entidad financiadora
Ministerio de Economía, Industria y Competitividad | Universitat Jaume I
Código del proyecto o subvención
PID2020-118021RB-I00/AEI/10.13039/501100011033 | UJI-B2022-48
Derechos de acceso
info:eu-repo/semantics/openAccess
Aparece en las colecciones
- EMC_Articles [813]
Excepto si se señala otra cosa, la licencia del ítem se describe como: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).