Classification of sEMG Biomedical Signals for Upper-Limb Rehabilitation Using the Random Forest Method-Reference-Cited by-同舟云学术

Classification of sEMG Biomedical Signals for Upper-Limb Rehabilitation Using the Random Forest Method

Published:2022-03-22 Issue: Volume: Page:
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Container-title:2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET)
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Author:

Briouza Sami¹,Gritli Hassene²,Khraief Nahla¹,Belghith Safya¹,Singh Dilbag³

Affiliation:

1. University of Tunis El Manar,Laboratory of Robotics, Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis,Tunis,Tunisia,1002

2. University of Carthage,Higher Institute of Information and Communication Technologies,Tunis,Tunisia,1164

3. Gwangju Institute of Science and Technology,Infonet Laboratory School of Electrical Engineering and Computer Science,South Korea

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9765836/9765827/09765871.pdf?arnumber=9765871

Reference37 articles.

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4. Random forest classification of finger movements using electromyogram (EMG) signals;f?nd?k;2020 IEEE SENSORS IEEE,2020

5. Electromyography data for non-invasive naturally-controlled robotic hand prostheses

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