Complexity Analysis of EMG Signals for Patients After Stroke During Robot-Aided Rehabilitation Training Using Fuzzy Approximate Entropy-Reference-Cited by-同舟云学术

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Complexity Analysis of EMG Signals for Patients After Stroke During Robot-Aided Rehabilitation Training Using Fuzzy Approximate Entropy

Published:2014-09 Issue:5 Volume:22 Page:1013-1019
ISSN:1534-4320
Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
language:
Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Sun Rui,Song Rong,Tong Kai-yu

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation

Link

http://xplorestaging.ieee.org/ielx7/7333/6893060/06663691.pdf?arnumber=6663691

Cited by 50 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Associating neuromotor outcomes at 12 months with wearable sensor measures collected during early infancy in rural Guatemala;Gait & Posture;2024-09

2. Data-Driven Identification of Stroke through Machine Learning Applied to Complexity Metrics in Multimodal Electromyography and Kinematics;Entropy;2024-07-07

3. Pedaling Asymmetry Reflected by Bilateral EMG Complexity in Chronic Stroke;Entropy;2024-06-23

4. Biomechanical Assessment Methods Used in Chronic Stroke: A Scoping Review of Non-Linear Approaches;Sensors;2024-04-06

5. Movement Diversity and Complexity Increase as Arm Impairment Decreases After Stroke: Quality of Movement Experience as a Possible Target for Wearable Feedback;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2024

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