Bao, T, Zaidi, A, Xie, S et al. (1 more author) (2019) Surface-EMG based Wrist Kinematics Estimation using Convolutional Neural Network. In: 2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN). 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN 2019)), 19-22 May 2019, Chicago, IL, USA. IEEE ISBN 978-1-5386-7477-2
Abstract
In the past decades, classical machine learning (ML) methods have been widely investigated in wrist kinematics estimation for the control of prosthetic hands. Currently deeper structures have shown great potential to further improve prediction accuracy. In this paper we present a single stream convolutional neural network (CNN) for mapping surface electromyography (sEMG) to wrist angles within three degrees-of-freedom (DOFs). Two types of two dimensional (2D) sEMG images are constructed in time domain and spectrum as CNN inputs, respectively. Six typical linear and nonlinear ML models are implemented for comparison, where four efficient time-spatial hand-crafted features are extracted to represent feature engineering. Experiment results with four able-bodied participants illustrate that CNN with 2D spectrum sEMG images can achieve highest accuracy in most testing sessions. In other sessions, it is still competitive to the most promising ML techniques. The core strength of deep learning (DL), i.e. feature learning via deep structures and efficient algorithms, is verified to be more powerful than classical feature engineering, particularly in smaller datasets.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. This is an author produced version of a paper published in 2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN). 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. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | sEMG; wrist kinematics estimation; machine learning; deep learning; convolutional neural networks |
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 EPSRC EP/S019219/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 28 Feb 2019 15:16 |
Last Modified: | 15 Nov 2019 02:26 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/BSN.2019.8771100 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:143040 |