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<p>The study highlights the crucial role of electromyogram signals (EMG) in recognizing hand and finger movements, and their application in controlling prosthetic limbs. Focusing on the development of human–machine interactions and rehabilitation devices, particularly robotic prostheses. This paper introduces an innovative model utilizing a convolutional neural network (CNN) for classifying fundamental hand grip movements. By converting EMG signals into channel-specific image spectrograms, the model achieved an unprecedented <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="521_2025_11456_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="39" /> </InlineMediaObject> <EquationSource Format="TEX">\(100\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>100</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation> accuracy in classifying 6 different movements, evaluated on the EMG hand gesture dataset from the UCI public repository. The results show superior performance compared to advanced methods, demonstrating the model’s potential as a cost-effective and precise control unit for accurately classifying hand grips from EMG signals.</p>

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Improvement in the classification of EMG signals through a convolutional neural network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月3日

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  • Cesar Benavides-Álvarez,
  • Eduardo Rodríguez-Martínez,
  • Carlos Avilés-Cruz,
  • Arturo Zúñiga-López,
  • Andrés Ferreyra-Ramírez,
  • Miriam Aguilar-Sánchez

摘要

The study highlights the crucial role of electromyogram signals (EMG) in recognizing hand and finger movements, and their application in controlling prosthetic limbs. Focusing on the development of human–machine interactions and rehabilitation devices, particularly robotic prostheses. This paper introduces an innovative model utilizing a convolutional neural network (CNN) for classifying fundamental hand grip movements. By converting EMG signals into channel-specific image spectrograms, the model achieved an unprecedented \(100\%\) 100 % accuracy in classifying 6 different movements, evaluated on the EMG hand gesture dataset from the UCI public repository. The results show superior performance compared to advanced methods, demonstrating the model’s potential as a cost-effective and precise control unit for accurately classifying hand grips from EMG signals.