A CNN-LSTM Hybrid Model for Wrist Kinematics Estimation Using Surface Electromyography

Bao, T, Zaidi, SAR, Xie, S et al. (2 more authors) (2020) A CNN-LSTM Hybrid Model for Wrist Kinematics Estimation Using Surface Electromyography. IEEE Transactions on Instrumentation and Measurement. ISSN 0018-9456

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Copyright, Publisher and Additional Information: © 2020 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.
Keywords: sEMG , wrist kinematics estimation , deep learning , convolutional neural network , long short-term memory network , hybrid model
Dates:
  • Accepted: 29 October 2020
  • Published (online): 9 November 2020
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds)
Funding Information:
FunderGrant number
EPSRC (Engineering and Physical Sciences Research Council)EP/S019219/1
Depositing User: Symplectic Publications
Date Deposited: 16 Nov 2020 10:39
Last Modified: 17 Nov 2020 12:39
Status: Published online
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Identification Number: https://doi.org/10.1109/tim.2020.3036654

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