Machine learning-based novel approach to classify the shoulder motion of upper limb amputees-Reference-Cited by-同舟云学术

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Machine learning-based novel approach to classify the shoulder motion of upper limb amputees

Published:2019-07 Issue:3 Volume:39 Page:857-867
ISSN:0208-5216
Container-title:Biocybernetics and Biomedical Engineering
language:en
Short-container-title:Biocybernetics and Biomedical Engineering

Author:

Amanpreet Kaur

Publisher

Elsevier BV

Subject

Biomedical Engineering

Reference60 articles.

1. Statistical analysis of amputations and trends in Korea;Kim;Prosthet Orthot Int,1996

2. Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees;Geng;J Neuroeng Rehabil,2012

3. Robot Control Using Electromyography (EMG) Signals of the Wrist;DaSalla;Appl Bionics Biomech,2015

4. Finger motion decoding using EMG signals corresponding various arm postures;You;Exp Neurobiol,2011

5. A bionic hand controlled by hand gesture recognition based on surface EMG signals: A preliminary study;Shi;Biocybern Biomed Eng,2018

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1. Continuous reach-to-grasp motion recognition based on an extreme learning machine algorithm using sEMG signals;Physical and Engineering Sciences in Medicine;2024-07-02

2. Predicting amputation using machine learning: A systematic review;PLOS ONE;2023-11-07

3. A hierarchical dynamic Bayesian learning network for EMG-based early prediction of voluntary movement intention;Scientific Reports;2023-03-23

4. Volitional control of upper-limb exoskeleton empowered by EMG sensors and machine learning computing;Array;2023-03

5. Surface Electromyography and Artificial Intelligence for Human Activity Recognition—A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future Implementation;IEEE Access;2023

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