Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25346
DC FieldValueLanguage
dc.contributorDepartment of Health Technology and Informatics-
dc.creatorKoo, TKK-
dc.creatorMak, AFT-
dc.date.accessioned2015-06-23T09:14:24Z-
dc.date.available2015-06-23T09:14:24Z-
dc.identifier.issn1050-6411-
dc.identifier.urihttp://hdl.handle.net/10397/25346-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectElbowen_US
dc.subjectElectromyographyen_US
dc.subjectMuscle mechanicsen_US
dc.subjectNeuromusculoskeletal modelingen_US
dc.titleFeasibility of using EMG driven neuromusculoskeletal model for prediction of dynamic movement of the elbowen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage12-
dc.identifier.epage26-
dc.identifier.volume15-
dc.identifier.issue1-
dc.identifier.doi10.1016/j.jelekin.2004.06.007-
dcterms.abstractNeuromusculoskeletal (NMS) modeling is a valuable tool in orthopaedic biomechanics and motor control research. To evaluate the feasibility of using electromyographic (EMG) signals with NMS modeling to estimate individual muscle force during dynamic movement, an EMG driven NMS model of the elbow was developed. The model incorporates dynamical equation of motion of the forearm, musculoskeletal geometry and musculotendon modeling of four prime elbow flexors and three prime elbow extensors. It was first calibrated to two normal subjects by determining the subject-specific musculotendon parameters using computational optimization to minimize the root mean square difference between the predicted and measured maximum isometric flexion and extension torque at nine elbow positions (0-120°of flexion with an increment of 15°). Once calibrated, the model was used to predict the elbow joint trajectories for three flexion/extension tasks by processing the EMG signals picked up by both surface and fine electrodes using two different EMG-to-activation processing schemes reported in the literature without involving any trajectory fitting procedures. It appeared that both schemes interpreted the EMG somewhat consistently but their prediction accuracy varied among testing protocols. In general, the model succeeded in predicting the elbow flexion trajectory in the moderate loading condition but over-drove the flexion trajectory under unloaded condition. The predicted trajectories of the elbow extension were noted to be continuous but the general shape did not fit very well with the measured one. Estimation of muscle activation based on EMG was believed to be the major source of uncertainty within the EMG driven model. It was especially so apparently when fine wire EMG signal is involved primarily. In spite of such limitation, we demonstrated the potential of using EMG driven neuromusculoskeletal modeling for non-invasive prediction of individual muscle forces during dynamic movement under certain conditions.-
dcterms.bibliographicCitationJournal of electromyography and kinesiology, 2005, v. 15, no. 1, p. 12-26-
dcterms.isPartOfJournal of electromyography and kinesiology-
dcterms.issued2005-
dc.identifier.scopus2-s2.0-11844251146-
dc.identifier.pmid15642650-
dc.identifier.eissn1873-5711-
dc.identifier.rosgroupidr24958-
dc.description.ros2004-2005 > Academic research: refereed > Publication in refereed journal-
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