A Variational Bayesian Gaussian Mixture-Nonnegative Matrix Factorization Model to Extract Movement Primitives for Robust Control-Reference-Cited by-同舟云学术

A Variational Bayesian Gaussian Mixture-Nonnegative Matrix Factorization Model to Extract Movement Primitives for Robust Control

Published:2022-12 Issue:6 Volume:52 Page:1258-1270
ISSN:2168-2291
Container-title:IEEE Transactions on Human-Machine Systems
language:
Short-container-title:IEEE Trans. Human-Mach. Syst.

Author:

Xie Hongbo¹^ORCID,Mengersen Kerrie²^ORCID,Di Changan³,Zhang Yongjian³,Lipman Justin⁴^ORCID,Van Huffel Sabine⁵^ORCID

Affiliation:

1. School of Information Science and Engineering, Yunnan University, Kunming, China

2. Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia

3. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, China

4. Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia

5. Department of Electrical Engineering, ESAT Stadius Division, KU Leuven, Leuven, Belgium

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Human-Computer Interaction,Signal Processing,Control and Systems Engineering,Human Factors and Ergonomics

Link

http://xplorestaging.ieee.org/ielx7/6221037/9950430/09854186.pdf?arnumber=9854186

Reference41 articles.

1. Muscle Synergies Extracted Using Principal Activations: Improvement of Robustness and Interpretability

2. Characterization of a Benchmark Database for Myoelectric Movement Classification

3. Alterations in upper limb muscle synergy structure in chronic stroke survivors

4. Task-discriminative space-by-time factorization of muscle activity

5. A robust measure of probability density function of various noises in electromyography (EMG) signal acquisition

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