Bao, T, Wang, C, Yang, P et al. (3 more authors) (2023) LSTM-AE for Domain Shift Quantification in Cross-day Upper-limb Motion Estimation Using Surface Electromyography. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31. pp. 2570-2580. ISSN 1558-0210
Abstract
Although deep learning (DL) techniques have been extensively researched in upper-limb myoelectric control, system robustness in cross-day applications is still very limited. This is largely caused by non-stable and time-varying properties of surface electromyography (sEMG) signals, resulting in domain shift impacts on DL models. To this end, a reconstruction-based method is proposed for domain shift quantification. Herein, a prevalent hybrid framework that combines a convolutional neural network (CNN) and a long short-term memory network (LSTM), i.e. CNN-LSTM, is selected as the backbone. The paring of auto-encoder (AE) and LSTM, abbreviated as LSTM-AE, is proposed to reconstruct CNN features. Based on reconstruction errors (RErrors) of LSTM-AE, domain shift impacts on CNN-LSTM can be quantified. For a thorough investigation, experiments were conducted in both hand gesture classification and wrist kinematics regression, where sEMG data were both collected in multi-days. Experiment results illustrate that, when the estimation accuracy degrades substantially in between-day testing sets, RErrors increase accordingly and can be distinct from those obtained in within-day datasets. According to data analysis, CNN-LSTM classification/regression outcomes are strongly associated with LSTM-AE errors. The average Pearson correlation coefficients could reach -0.986±0.014 and -0.992±0.011, respectively.
Metadata
Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2023. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. | ||||
Keywords: | sEMG, deep learning, long short-term memory network, auto-encoder, domain shift quantification | ||||
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) | ||||
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Depositing User: | Symplectic Publications | ||||
Date Deposited: | 08 Jun 2023 13:05 | ||||
Last Modified: | 19 Jul 2023 13:44 | ||||
Published Version: | https://ieeexplore.ieee.org/document/10138591 | ||||
Status: | Published | ||||
Publisher: | IEEE | ||||
Identification Number: | https://doi.org/10.1109/TNSRE.2023.3281455 |