Ai, Q, Ding, B, Liu, Q et al. (1 more author) (2016) A subject-specific EMG-driven musculoskeletal model for applications in lower-limb rehabilitation robotics. International Journal of Humanoid Robotics, 13 (03). 1650005. ISSN 0219-8436
Abstract
Robotic devices have great potential in physical therapy owing to their repeatability, reliability and cost economy. However, there are great challenges to realize active control strategy, since the operator’s motion intention is uneasy to be recognized by robotics online. The purpose of this paper is to propose a subject-specific electromyography (EMG)-driven musculoskeletal model to estimate subject’s joint torque in real time, which can be used to detect his/her motion intention by forward dynamics, and then to explore its potential applications in rehabilitation robotics control. The musculoskeletal model uses muscle activation dynamics to extract muscle activation from raw EMG signals, a Hill-type muscle-tendon model to calculate muscle contraction force, and a proposed subject-specific musculoskeletal geometry model to calculate muscular moment arm. The parameters of muscle activation dynamics and muscle-tendon model are identified by off-line optimization methods in order to minimize the differences between the estimated muscular torques and the reference torques. Validation experiments were conducted on six healthy subjects to evaluate the proposed model. Experimental results demonstrated the model’s ability to predict knee joint torque with the coefficient of determination (R2) value of 0.934±0.0130.934±0.013 and the normalized root-mean-square error (RMSE) of 11.58%±1.44%11.58%±1.44%.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 World Scientific Publishing Co. This is an author produced version of a paper published in International Journal of Humanoid Robotics. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | EMG signals; musculoskeletal modeling; rehabilitation robotics; control strategies |
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) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Jan 2018 16:17 |
Last Modified: | 10 Jan 2018 09:28 |
Status: | Published |
Publisher: | World Scientific |
Identification Number: | 10.1142/S0219843616500055 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:125855 |