Application of Min-Max Normalization on Subject-Invariant EMG Pattern Recognition-Reference-Cited by-同舟云学术

Application of Min-Max Normalization on Subject-Invariant EMG Pattern Recognition

Published:2022 Issue: Volume:71 Page:1-12
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Islam Md. Johirul¹^ORCID,Ahmad Shamim²^ORCID,Haque Fahmida³^ORCID,Reaz Mamun Bin Ibne⁴^ORCID,Bhuiyan Mohammad Arif Sobhan⁵^ORCID,Islam Md. Rezaul⁶^ORCID

Affiliation:

1. Department of Physics, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh

2. Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh

3. Laboratory of Emotions Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warszawa, Poland

4. Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia

5. Department of Electrical and Electronic Engineering, Xiamen University Malaysia, Sepang, Malaysia

6. Department of Electrical and Electronic Engineering, University of Rajshahi, Rajshahi, Bangladesh

Funder

Xiamen University Malaysia

Information and Communication Technology Division, Ministry of Posts, Telecommunications, and Information Technology, Government of Bangladesh

Universiti Kebangsaan Malaysia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/9717300/09941001.pdf?arnumber=9941001

Reference47 articles.

1. Forearm Orientation and Muscle Force Invariant Feature Selection Method for Myoelectric Pattern Recognition

2. Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features

3. A novel extreme learning machine for dimensionality reduction on finger movement classification using sEMG

4. Inter-Subject Domain Adaptation for CNN-Based Wrist Kinematics Estimation Using sEMG

5. A Fault-Tolerant Algorithm to Enhance Generalization of EMG-based Pattern Recognition for Lower Limb Movement

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