Compression of EMG Signals Using Deep Convolutional Autoencoders-Reference-Cited by-同舟云学术

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Compression of EMG Signals Using Deep Convolutional Autoencoders

Published:2022-07 Issue:7 Volume:26 Page:2888-2897
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Dinashi Kimia¹^ORCID,Ameri Ali²^ORCID,Akhaee Mohammad Ali¹^ORCID,Englehart Kevin³^ORCID,Scheme Erik³^ORCID

Affiliation:

1. Department of Electrical Engineering, University of Tehran, Tehran, Iran

2. Department of Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

3. Institute of Biomedical Engineering, University of New Brunswick, Fredericton, Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/9813470/09678006.pdf?arnumber=9678006

Reference34 articles.

1. Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use

2. Real-time, simultaneous myoelectric control using a convolutional neural network

3. High efficiency video coding (HEVC),2021

4. Overview of the High Efficiency Video Coding (HEVC) Standard

5. A Bus-Based Smart Myoelectric Electrode/Amplifier—System Requirements

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