Machine Learning and Signal Processing Based Analysis of sEMG Signals for Daily Action Classification-Reference-Cited by-同舟云学术

Machine Learning and Signal Processing Based Analysis of sEMG Signals for Daily Action Classification

Published:2022 Issue: Volume:10 Page:40506-40516
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Sadiq Ayesha¹^ORCID,Khawaja Sajid Gul¹^ORCID,Akram Muhammad Usman¹,Alghamdi Norah Saleh²^ORCID,Khan Aimal¹,Shaukat Arsalan¹^ORCID

Affiliation:

1. Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad, Pakistan

2. Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

Funder

Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia, through the Princess Nourah Bint Abdulrahman University Researchers Supporting Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09755997.pdf?arnumber=9755997

Reference26 articles.

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2. Development of a deep neural network for automated electromyographic pattern classification

3. Classification of multichannel surface-electromyography signals based on convolutional neural networks

4. Analysis of EMG Signals in Aggressive and Normal Activities by Using Higher-Order Spectra

5. An efficient method for analysis of EMG signals using improved empirical mode decomposition

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