Harnessing Machine Learning and Physiological Knowledge for a Novel EMG-Based Neural-Machine Interface-Reference-Cited by-同舟云学术

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Harnessing Machine Learning and Physiological Knowledge for a Novel EMG-Based Neural-Machine Interface

Published:2023-04 Issue:4 Volume:70 Page:1125-1136
ISSN:0018-9294
Container-title:IEEE Transactions on Biomedical Engineering
language:
Short-container-title:IEEE Trans. Biomed. Eng.

Author:

Berman Joseph¹^ORCID,Hinson Robert²^ORCID,Lee I-Chieh²^ORCID,Huang He³^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, North Carolina State University, USA

2. UNC/NC State Joint Department of Biomedical Engineering, North Carolina State University, USA

3. UNC/NC State Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC, USA

Funder

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

https://ieeexplore.ieee.org/ielam/10/10078065/9905918-aam.pdf

Reference42 articles.

1. Sensitivity of Smoothness Measures to Movement Duration, Amplitude, and Arrests

2. Real-Time and Simultaneous Control of Artificial Limbs Based on Pattern Recognition Algorithms

3. Using Reinforcement Learning to Estimate Human Joint Moments From Electromyography or Joint Kinematics: An Alternative Solution to Musculoskeletal-Based Biomechanics

4. A real-time, practical sensor fault-tolerant module for robust EMG pattern recognition

5. Linear and Nonlinear Regression Techniques for Simultaneous and Proportional Myoelectric Control

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3. Graph-Driven Simultaneous and Proportional Estimation of Wrist Angle and Grasp Force via High-Density EMG;IEEE Journal of Biomedical and Health Informatics;2024-05

4. NSF DARE—Transforming modeling in neurorehabilitation: Four threads for catalyzing progress;Journal of NeuroEngineering and Rehabilitation;2024-04-03

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