Zhao, Y, Dehghani-Sanij, AA and Xie, S orcid.org/0000-0002-8082-9112 (2021) Electromyography-based Adaptive Cooperative Control for a Wrist Orthosis. In: 2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). 2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 26-28 Nov 2021, Shanghai, China. IEEE ISBN 978-1-6654-3153-8
Abstract
This paper proposes an adaptive cooperative control method for a wrist orthosis, consisting of a trajectory tracking controller, an admittance controller integrated with an electromyography (EMG)-driven musculoskeletal model-based approach. The admittance controller adaptively alters the reference trajectory based on the estimated joint torque by the EMG-driven musculoskeletal model. The admittance parameters are regulated by accessing the wrist joint condition in real-time. Three experiments are conducted including, trajectory tracking control (TTC), fixed cooperative control (FCC), adaptive cooperative control (ACC) with two cooperative ratios of 0.3 and 0.6 respectively. Preliminary results demonstrate that the cooperative control strategies have smaller root-mean-square-errors compared with the TTC when the subject’s intention is detected. The proposed method can modify the wrist orthosis’s compliance in real-time in response to the wrist joint stiffness changes, which shows its potential to improve the efficiency and safety in rehabilitation.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2021,IEEE. 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. |
Dates: |
|
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 (Engineering and Physical Sciences Research Council) EP/S019219/1 Royal Society IEC\NSFC\191095 |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 May 2022 15:16 |
Last Modified: | 17 May 2022 15:16 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/m2vip49856.2021.9665116 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:186906 |