Zhao, Y, Zhang, J, Li, Z orcid.org/0000-0003-2583-5082 et al. (4 more authors) (2022) Computational Efficient Personalised EMG-Driven Musculoskeletal Model of Wrist Joint. IEEE Transactions on Instrumentation and Measurement, 72. 4001410. ISSN 0018-9456
Abstract
Myoelectric control has gained much attention which translates the human intentions into control commands for exoskeletons. The electromyogram (EMG)-driven musculoskeletal (MSK) model shows prominent performance given its ability to interpret the underlying neuromechanical processes among the musculoskeletal system. This model-based scheme contains inherent physiological parameters, e.g., isometric muscle force, tendon slack length, or optimal muscle fibre length, which need to be tailored for each individual via minimising the differences between the experimental measurement and model estimation. However, the creation of the personalised EMG-driven MSK model through the evolutionary algorithms is time-consuming, hurdling the use of the EMG-driven MSK model in practical scenarios. This paper proposes a computational efficient optimisation method to estimate the subject-specific physiological parameters for a wrist MSK model based on the direct collocation method. By constraining control variables to the experimentally measured EMG signals and introducing the physiological parameters into control variables, fast optimisation is achieved by identifying the discretised parameters at each grid simultaneously. Experimental evaluations on 12 healthy subjects are performed. Results demonstrate the proposed method outperforms the baseline optimisation algorithms used in the literature, including genetic algorithm, simulated annealing algorithm, and particle swarm optimisation algorithm. The proposed direct collocation method shows the possibility to alleviate the costly optimisation procedure and facilitate the use of the MSK model in practical applications.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IEEE. This is an author produced version of an article published in IEEE Transactions on Instrumentation and Measurement, made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Direct collocation (DC) method, electromyogram (EMG)-driven musculoskeletal (MSK) model, parameter optimization, personalization, wrist joint |
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) The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Biomedical Sciences (Leeds) |
Funding Information: | Funder Grant number EU - European Union 101023097 EPSRC (Engineering and Physical Sciences Research Council) EP/S019219/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Nov 2022 15:12 |
Last Modified: | 25 Jan 2023 15:56 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TIM.2022.3225023 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193399 |