sEMG-Based Hand-Gesture Classification Using a Generative Flow Model

Conventional pattern-recognition algorithms for surface electromyography (sEMG)-based hand-gesture classification have difficulties in capturing the complexity and variability of sEMG. The deep structures of deep learning enable the method to learn high-level features of data to improve both accurac...

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Detalles Bibliográficos
Autores principales: Sun, Wentao, Liu, Huaxin, Tang, Rongyu, Lang, Yiran, He, Jiping, Huang, Qiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515175/
https://www.ncbi.nlm.nih.gov/pubmed/31027292
http://dx.doi.org/10.3390/s19081952

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515175/
https://www.ncbi.nlm.nih.gov/pubmed/31027292
http://dx.doi.org/10.3390/s19081952

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