Meng, W, Ding, B, Zhou, Z et al. (2 more authors) (2015) An EMG-based force prediction and control approach for robot-assisted lower limb rehabilitation. In: Proceedings of the 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 05-08 Oct 2014, San Diego, CA, USA. IEEE , pp. 2198-2203. ISBN 978-1-4799-3840-7
Abstract
This paper proposes an electromyography (EMG)-based method for online force prediction and control of a lower limb rehabilitation robot. Root mean square (RMS) features of EMG signals from four muscles of the lower limb are used as the inputs to a support vector regression (SVR) model to estimate the human-robot interaction force. The autoregressive algorithm is utilized to construct the relationship between EMG signals and the impact force. Combining the force prediction model with the position-based impedance controller, the robot can be controlled to track the desired force of the lower limb, and so as to achieve an adaptive and active rehabilitation mode, which is adaptable to the individual muscle strength and movement ability. Finally, the method was validated through experiments on a healthy subject. The results show that the EMG-based SVR model can predict the lower limb force accurately and the robot can be controlled to track the estimated force by using simplified impedance model.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2014 by the Institute of Electrical and Electronics Engineers, Inc. This is an author produced version of a paper published in Proceedings of the 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 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: | EMG; force prediction; rehabilitation robot; SVR; impedance control |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Jun 2018 15:40 |
Last Modified: | 26 Jun 2018 15:40 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/SMC.2014.6974250 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:125856 |