Gesture Recognition Using MLP-Mixer With CNN and Stacking Ensemble for sEMG Signals-Reference-Cited by-同舟云学术

Gesture Recognition Using MLP-Mixer With CNN and Stacking Ensemble for sEMG Signals

Published:2024-02-15 Issue:4 Volume:24 Page:4960-4968
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Shen Shu¹^ORCID,Li Minglei²^ORCID,Mao Fan²,Chen Xinrong³^ORCID,Ran Ran²^ORCID

Affiliation:

1. School of Computer Science and the Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing University of Posts and Telecommunications, Nanjing, China

2. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China

3. Academy for Engineering and Technology, Fudan University, Shanghai, China

Funder

National Natural Science Foundation of China

Nature Science Research of Jiangsu Higher Education Institutions of China

Postgraduate Research and Practice Innovation Program of Jiangsu Province

Postgraduate Education Reform Project of Nanjing University of Posts and Telecommunications

Open Project Program of the State Key Lab of CAD & CG of Zhejiang University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10433867/10381626.pdf?arnumber=10381626

Reference33 articles.

1. Identifying the Motor Neuron Disease in EMG Signal Using Time and Frequency Domain Features with Comparison

2. Towards the sEMG hand: internet of things sensors and haptic feedback application

3. Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

4. A Study on the Classification Effect of sEMG Signals in Different Vibration Environments Based on the LDA Algorithm

5. SeNic: An Open Source Dataset for sEMG-Based Gesture Recognition in Non-Ideal Conditions

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