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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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| Publicado no: | Sensors (Basel) |
|---|---|
| Main Authors: | , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
MDPI
2019
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6515175/ https://ncbi.nlm.nih.gov/pubmed/31027292 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s19081952 |
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