A deep Kalman filter network for hand kinematics estimation using sEMG

Bao, T orcid.org/0000-0002-1103-2660, Zaidi, SAR orcid.org/0000-0003-1969-3727, Xie, S orcid.org/0000-0003-2641-2620 et al. (3 more authors) (2021) A deep Kalman filter network for hand kinematics estimation using sEMG. Pattern Recognition Letters, 143. pp. 88-94. ISSN 0167-8655



Copyright, Publisher and Additional Information: © 2021, Elsevier. All rights reserved. This is an author produced version of an article published in Pattern Recognition Letters. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: sEMG; Hand kinematics estimation; Sequential regression; LSTM; Kalman filter
  • Accepted: 11 January 2021
  • Published (online): 13 January 2021
  • Published: March 2021
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
Royal SocietyICA\R1\180203
EPSRC (Engineering and Physical Sciences Research Council)EP/S019219/1
Royal SocietyIEC\NSFC\191095
Depositing User: Symplectic Publications
Date Deposited: 15 Jan 2021 15:50
Last Modified: 13 Jan 2022 01:40
Status: Published
Publisher: Elsevier
Identification Number: https://doi.org/10.1016/j.patrec.2021.01.001