Liu, Q, Ma, L, Ai, Q et al. (2 more authors) (2018) Knee Joint Angle Prediction Based on Muscle Synergy Theory and Generalized Regression Neural Network. In: 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE/ASME International Conference on AIM, 09-12 Jul 2018, Auckland, New Zealand. IEEE , pp. 28-32. ISBN 9781538618547
Abstract
Continuous joint motion estimation plays an important part in accomplishing more compliant and safer human-machine interaction (HMI). Surface electromyogram (sEMG) signals, which contain abundant motion information, can be used as a source for continuous joint motion estimation. In this paper, a knee joint angle prediction system based on muscle synergy theory and generalized regression neural network (GRNN) was proposed. The wavelet transform threshold method was used for sEMG signals and angle trajectories denoising. The time-domain features wave-length extracted from four-channel sEMG signals were decomposed into a synergy matrix and an activation coefficient matrix by using nonnegative matrix factorization based on muscle synergy theory. A GRNN based on golden-section search was employed to build the activation model mapping from the activation coefficients to the knee joint angles, so as to realize the continuous knee joint angle estimation. The experimental results show that the average coefficient of determination is 0.933. In addition, a user graphic interface based on the Java platform was designed to display the dynamic sEMG data and predicted knee joint angles in real time.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Oct 2018 10:15 |
Last Modified: | 10 Oct 2018 07:36 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/AIM.2018.8452230 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:136831 |