Compression of EMG Signals Using Deep Convolutional Autoencoders

Efficient storage and transmission of electromyogram (EMG) data are important for emerging applications such as telemedicine and big data, as a vital tool for further advancement of the field. However, due to limitations in internet speed and hardware resources, transmission and storage of EMG data...

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Veröffentlicht in:	IEEE journal of biomedical and health informatics Jg. 26; H. 7; S. 2888 - 2897
Hauptverfasser:	Dinashi, Kimia, Ameri, Ali, Akhaee, Mohammad Ali, Englehart, Kevin, Scheme, Erik
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.07.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Big Data Classification Compression Convolution convolutional autoencoder Convolutional codes Data compression Decoding Deep learning Electrodes Electromyography EMG Encoding Reconstruction Training Video compression Wrist
ISSN:	2168-2194, 2168-2208, 2168-2208
Online-Zugang:	Volltext
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