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dc.contributor.authorSartori, Massimo
dc.contributor.authorLlyod, David G
dc.contributor.authorFarina, Dario
dc.date.accessioned2018-05-28T02:46:29Z
dc.date.available2018-05-28T02:46:29Z
dc.date.issued2016
dc.identifier.issn0018-9294
dc.identifier.doi10.1109/TBME.2016.2538296
dc.identifier.urihttp://hdl.handle.net/10072/100022
dc.description.abstractObjectives: The development of neurorehabilitation technologies requires the profound understanding of the mechanisms underlying an individual's motor ability and impairment. A major factor limiting this understanding is the difficulty of bridging between events taking place at the neurophysiologic level (i.e., motor neuron firings) with those emerging at the musculoskeletal level (i.e. joint actuation), in vivo in the intact moving human. This review presents emerging model-based methodologies for filling this gap that are promising for developing clinically viable technologies. Methods: We provide a design overview of musculoskeletal modeling formulations driven by recordings of neuromuscular activity with a critical comparison to alternative model-free approaches in the context of neurorehabilitation technologies. We present advanced electromyography-based techniques for interfacing with the human nervous system and model-based techniques for translating the extracted neural information into estimates of motor function. Results: We introduce representative application areas where modeling is relevant for accessing neuromuscular variables that could not be measured experimentally. We then show how these variables are used for designing personalized rehabilitation interventions, biologically inspired limbs, and human-machine interfaces. Conclusion: The ability of using electrophysiological recordings to inform biomechanical models enables accessing a broader range of neuromechanical variables than analyzing electrophysiological data or movement data individually. This enables understanding the neuromechanical interplay underlying in vivo movement function, pathology, and robot-assisted motor recovery. Significance: Filling the gap between our understandings of movement neural and mechanical functions is central for addressing one of the major challenges in neurorehabilitation: personalizing current technologies and interventions to an individual's anatomy and impairment.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofpagefrom879
dc.relation.ispartofpageto893
dc.relation.ispartofissue5
dc.relation.ispartofjournalIEEE Transactions on Biomedical Engineering
dc.relation.ispartofvolume63
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchBiomedical engineering
dc.subject.fieldofresearchMedical devices
dc.subject.fieldofresearchBiomechanics
dc.subject.fieldofresearchcode4602
dc.subject.fieldofresearchcode4003
dc.subject.fieldofresearchcode400308
dc.subject.fieldofresearchcode420701
dc.titleNeural Data-Driven Musculoskeletal Modeling for Personalized Neurorehabilitation Technologies
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttps://creativecommons.org/licenses/by/3.0/
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Authors 2016. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
gro.hasfulltextFull Text
gro.griffith.authorLloyd, David


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