Optimizing sEMG Gesture Recognition: Leveraging Channel Selection and Feature Compression for Improved Accuracy and Computational Efficiency

Optimizing sEMG Gesture Recognition: Leveraging Channel Selection and Feature Compression for Improved Accuracy and Computational Efficiency

In the task of upper-limb pattern recognition, effective feature extraction, channel selection, and classification methods are crucial for the construction of an efficient surface electromyography (sEMG) signal classification framework. However, existing deep learning models often face limitations d...

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Bibliographic Details
Main Authors: Yinxi Niu, Wensheng Chen, Hui Zeng, Zhenhua Gan, Baoping Xiong
Format: Article
Language:English
Published: MDPI AG 2024-04-01
Series:Applied Sciences
Subjects:
sEMG
hand gestures classification
channel feature compression
channel feature selection
Online Access:https://www.mdpi.com/2076-3417/14/8/3389

Internet

https://www.mdpi.com/2076-3417/14/8/3389

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