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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Ngā kaituhi matua:	Lingzhi Lin, Yuqian Dai, Guodao Zhang, Yisu Ge, Abdulilah Mohammad Mayet, Xiaotian Pan, Genfu Yang, Mian Lin
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	Elsevier 2025-09-01
Rangatū:	Results in Engineering
Ngā marau:	Biomedical signals Time and frequency features Artificial neural network EMG signals
Urunga tuihono:	http://www.sciencedirect.com/science/article/pii/S2590123025026714
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

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

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