Lower Limb Activity Recognition Based on sEMG Using Stacked Weighted Random Forest-Reference-Cited by-同舟云学术

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Lower Limb Activity Recognition Based on sEMG Using Stacked Weighted Random Forest

Published:2024 Issue: Volume:32 Page:166-177
ISSN:1534-4320
Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
language:
Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Shen Cheng¹^ORCID,Pei Zhongcai¹^ORCID,Chen Weihai²^ORCID,Wang Jianhua¹^ORCID,Wu Xingming¹^ORCID,Chen Jianer³^ORCID

Affiliation:

1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

2. School of Electrical Engineering and Automation, Anhui University, Hefei, China

3. Third Clinical Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, China

Funder

National Nature Science Foundation

Key Research and Development Program of Zhejiang Province

Scientific Research Project of Agriculture and Social Development of Hangzhou

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation

Link

http://xplorestaging.ieee.org/ielx7/7333/10398877/10373087.pdf?arnumber=10373087

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4. Review of Vision-Based Environmental Perception for Lower-Limb Exoskeleton Robots;Biomimetics;2024-04-22

5. Variational Mode Decomposition and Empirical Wavelet Transform-Based Feature Extraction and Ensemble Classifier for Lower Limb Movement Prediction With Surface Electromyography Signal;IEEE Access;2024

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