Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals
By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore,...
Main Authors: | Lin Chen, Jianting Fu, Yuheng Wu, Haochen Li, Bin Zheng |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-01-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/20/3/672 |
Similar Items
-
Data Augmentation of Surface Electromyography for Hand Gesture Recognition
by: Panagiotis Tsinganos, et al.
Published: (2020-08-01) -
High-Performance Surface Electromyography Armband Design for Gesture Recognition
by: Ruihao Zhang, et al.
Published: (2023-05-01) -
putEMG—A Surface Electromyography Hand Gesture Recognition Dataset
by: Piotr Kaczmarek, et al.
Published: (2019-08-01) -
From Forearm to Wrist: Deep Learning for Surface Electromyography-Based Gesture Recognition
by: Jiayuan He, et al.
Published: (2024-01-01) -
sEMG-Based Gesture Recognition With Embedded Virtual Hand Poses and Adversarial Learning
by: Yu Hu, et al.
Published: (2019-01-01)