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

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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Bibliographic Details
Main Authors: Wentao Sun, Huaxin Liu, Rongyu Tang, Yiran Lang, Jiping He, Qiang Huang
Format: Article
Language:English
Published: MDPI AG 2019-04-01
Series:Sensors
Subjects:
surface electromyography
hand-gesture classification
generative flow model
Online Access:https://www.mdpi.com/1424-8220/19/8/1952

Internet

https://www.mdpi.com/1424-8220/19/8/1952

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