| Real-Time Upper Limb Motion Estimation from Surface Electromyography and Joint Angular Velocities using an Artificial Neural Network for Human-Machine Cooperation. | |
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MedLine Citation:
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PMID: 21558060 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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A current challenge with human-machine cooperation systems is to estimate human motions to facilitate natural cooperation and safety of the human. It is a logical approach to estimate the motions from their sources (skeletal muscles); thus, we employed surface electromyography (SEMG) to estimate body motions. In this paper, we investigated a cooperative manipulation control by an upper limb motion estimation method using SEMG and joint angular velocities. The SEMG signals from five upper limb muscles and angular velocities of the limb joints were used to approximate the flexion-extension of the limb in the 2D sagittal plane. The experimental results showed that the proposed estimation method provides acceptable performance of the motion estimation [normalized root mean square error (NRMSE) < 0.15, correlation coefficient (CC) > 0.9] under the noncontact condition. From the analysis of the results, we found the necessity of the angular velocity input and estimation error feedback due to physical contact. Our results suggest that the estimation method can be useful for a natural human-machine cooperation control. |
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Authors:
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S Kwon; J Kim |
Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-5-10 |
Journal Detail:
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Title: IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society Volume: - ISSN: 1558-0032 ISO Abbreviation: - Publication Date: 2011 May |
Date Detail:
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Created Date: 2011-5-11 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9712259 Medline TA: IEEE Trans Inf Technol Biomed Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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