E2CNN: An Efficient Concatenated CNN for Classification of Surface EMG Extracted From Upper Limb

Surface electromyography is a bioelectrical indicator that emerges during muscle contraction and has been widely used in a variety of clinical applications. Several prosthetic control applications can benefit from the analysis based on the classification of surface electromyography (sEMG) signals. H...

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Vydané v:	IEEE sensors journal Ročník 23; číslo 8; s. 8989 - 8996
Hlavní autori:	Qureshi, Muhammad Farrukh, Mushtaq, Zohaib, Rehman, Muhammad Zia Ur, Kamavuako, Ernest Nlandu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 15.04.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accuracy Artificial neural networks Bioelectricity Classification Concatenation Convolution convolutional neural network (CNN) Convolutional neural networks Datasets Electromyography Log-Mel spectrogram (LMS) Muscular function NinaPro DB1 Prostheses Prosthetics Real time Sensors Spectrogram Spectrograms surface electromyography (sEMG) Thumb
ISSN:	1530-437X, 1558-1748
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