Zhao, Y, Li, Z, Zhang, Z orcid.org/0000-0003-0204-3867 et al. (2 more authors) (2021) A Direct Collocation method for optimization of EMG-driven wrist muscle musculoskeletal model. In: 2021 IEEE International Conference on Robotics and Automation (ICRA). 2021 IEEE International Conference on Robotics and Automation (ICRA), 30 May - 05 Jun 2021, Online. IEEE , pp. 1759-1765. ISBN 978-1-7281-9078-5
Abstract
EMG-driven musculoskeletal model has been broadly used to detect human intention in rehabilitation robots. This approach computes muscle-tendon force and translates it to the joint kinematics. However, the muscle-tendon parameters of the musculoskeletal model are difficult to measure in vivo and varied across subjects. In this study, a direct collocation (DC) method is proposed to optimize the subject-specific parameters in a wrist musculoskeletal model. The resultant optimized parameters are used to estimate the wrist flexion/extension motion. The estimation performance is compared with the parameters optimized by the genetic algorithm. Experiment results show that the DC methods have a similar performance compared with GA, in which the mean correlation are 0.96 and 0.93 for the genetic algorithm and DC method respectively. But the direction collocation method requires less optimization time.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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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. |
Keywords: | Wrist , In vivo , Correlation , Computational modeling , Kinematics , Rehabilitation robotics , Programming |
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 (Engineering and Physical Sciences Research Council) EP/S019219/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Apr 2022 10:45 |
Last Modified: | 30 Apr 2022 02:55 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/icra48506.2021.9561424 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:185594 |