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dc.contributor.authorDemircan, E.
dc.contributor.authorKhatib, O.
dc.contributor.authorWheeler, J.
dc.contributor.authorDelp, S.
dc.date.accessioned2016-02-28T05:51:40Z
dc.date.available2016-02-28T05:51:40Z
dc.date.issued2009-09
dc.identifier.citationDemircan E, Khatib O, Wheeler J, Delp S (2009) Reconstruction and EMG-informed control, simulation and analysis of human movement for athletics: Performance improvement and injury prevention. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Available: http://dx.doi.org/10.1109/IEMBS.2009.5333148.
dc.identifier.pmid19964175
dc.identifier.doi10.1109/IEMBS.2009.5333148
dc.identifier.urihttp://hdl.handle.net/10754/599466
dc.description.abstractIn this paper we present methods to track and characterize human dynamic skills using motion capture and electromographic sensing. These methods are based on task-space control to obtain the joint kinematics and extract the key physiological parameters and on computed muscle control to solve the muscle force distribution problem. We also present a dynamic control and analysis framework that integrates these metrics for the purpose of reconstructing and analyzing sports motions in real-time.
dc.description.sponsorshipThis work was supported in part bythe Simbios National Center for Biomedical Computing Grant(http://simbios.stanford.edu/, NIH GM072970) and KAUST (KingAbdullah University of Science and Technology).
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subject.meshTask Performance and Analysis
dc.titleReconstruction and EMG-informed control, simulation and analysis of human movement for athletics: Performance improvement and injury prevention
dc.typeConference Paper
dc.identifier.journal2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
dc.identifier.pmcidPMC4479296
dc.contributor.institutionMechanical Engineering Department, Stanford University, Stanford, CA 94305, USA. emeld@stanford.edu


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