Cargando…
Myoelectric Pattern Recognition Using Gramian Angular Field and Convolutional Neural Networks for Muscle–Computer Interface
In the field of the muscle–computer interface, the most challenging task is extracting patterns from complex surface electromyography (sEMG) signals to improve the performance of myoelectric pattern recognition. To address this problem, a two-stage architecture, consisting of Gramian angular field (...
Autores principales: | Fan, Junjun, Wen, Jiajun, Lai, Zhihui |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007307/ https://www.ncbi.nlm.nih.gov/pubmed/36904918 http://dx.doi.org/10.3390/s23052715 |
Ejemplares similares
-
Lightweight Gramian Angular Field classification for edge internet of energy applications
por: Alsalemi, Abdullah, et al.
Publicado: (2022) -
Advancing Fault Detection in HVAC Systems: Unifying Gramian Angular Field and 2D Deep Convolutional Neural Networks for Enhanced Performance
por: Tun, Wunna, et al.
Publicado: (2023) -
ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States
por: Camara, Carmen, et al.
Publicado: (2023) -
A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field
por: Han, Bin, et al.
Publicado: (2021) -
Novel domestic building energy consumption dataset: 1D timeseries and 2D Gramian Angular Fields representation
por: Alsalemi, Abdullah, et al.
Publicado: (2023)