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
Abstract
In human-machine interfaces (HMI), deep learning (DL) techniques such as convolutional neural networks (CNN), long-short term memory networks (LSTM) and the hybrid CNN-LSTM framework have been exploited for hand kinematics estimation using surface electromyography (sEMG). However, these DL techniques only capture the relationship between sEMG and hand kinematics, but ignores the prior knowledge of the system. By contrast, Kalman filter (KF) can apply Kalman gain to combine the internal transition model and the observation model effectively. To this end, we propose a novel architecture named deep Kalman filter network (DKFN), in which we utilize CNN to extract high-level features from sEMG and employ a LSTM-based Kalman filter process (LSTM-KF) to conduct sequential regression. In particular, LSTM-KF adopts the computational graph of KF but estimates parameters of the transition/observation model and the Kalman gain from data using LSTM modules. With this process, the advantages of KF and LSTM can be exploited jointly. Experimental results demonstrate that the proposed DKFN can outperform CNN and CNN-LSTM in the sequential regression for wrist/fingers kinematics estimation.
Metadata
Item Type: | Article |
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Authors/Creators: |
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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 |
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) > Robotics, Autonomous Systems & Sensing (Leeds) |
Funding Information: | Funder Grant number Royal Society ICA\R1\180203 EPSRC (Engineering and Physical Sciences Research Council) EP/S019219/1 Royal Society IEC\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: | 10.1016/j.patrec.2021.01.001 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:170019 |
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