Low-computational EMG gesture recognition for prosthetic control via handcrafted features and lightweight MLP

Low-computational EMG gesture recognition for prosthetic control via handcrafted features and lightweight MLP

This study introduces an efficient and accurate approach to classifying electromyography (EMG) signals for advanced prosthetic control and rehabilitation, addressing the need for practical, real-time human-computer interaction (HCI) systems. Unlike conventional deep learning methods that require sub...

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Bibliographic Details
Main Authors: Lingzhi Lin, Yuqian Dai, Guodao Zhang, Yisu Ge, Abdulilah Mohammad Mayet, Xiaotian Pan, Genfu Yang, Mian Lin
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Results in Engineering
Subjects:
Biomedical signals
Time and frequency features
Artificial neural network
EMG signals
Online Access:http://www.sciencedirect.com/science/article/pii/S2590123025026714
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http://www.sciencedirect.com/science/article/pii/S2590123025026714

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