Asymmetric Effects of Different Training-Testing Mismatch Types on Myoelectric Regression via Deep Learning-Reference-Cited by-同舟云学术

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Asymmetric Effects of Different Training-Testing Mismatch Types on Myoelectric Regression via Deep Learning

Published:2023-04 Issue:4 Volume:27 Page:1857-1868
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Becman Eric Cito¹^ORCID,Driemeier Larissa¹^ORCID,Levin Oron²^ORCID,Swinnen Stephan²^ORCID,Forner-Cordero Arturo¹^ORCID

Affiliation:

1. Biomechatronics Laboratory, Department of Mechatronics and Mechanical Systems, Polytechnic School, University of São Paulo, São Paulo, Brazil

2. Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10092240/10024276.pdf?arnumber=10024276

Reference43 articles.

1. Toward Robust, Adaptiveand Reliable Upper-Limb Motion Estimation Using Machine Learning and Deep Learning–A Survey in Myoelectric Control

2. EMG feature extraction for tolerance of white Gaussian noise;phinyomark;Proc Int Workshop Symp Sci Technol,0

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

4. Exploring the Relation Between EMG Pattern Recognition and Sampling Rate Using Spectrogram

5. Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses

Cited by 2 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. A comparison study of myoelectric regression performances when estimating different types of joint kinematic data;Expert Systems with Applications;2024-11

2. An adaptive cost-sensitive learning approach in neural networks to minimize local training–test class distributions mismatch;Intelligent Systems with Applications;2024-03

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