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dc.contributor.authorSong, Hyungeun
dc.contributor.authorIsrael, Erica
dc.contributor.authorSrinivasan, Shriya Sruthi
dc.contributor.authorHerr, Hugh M
dc.date.accessioned2020-09-08T21:41:52Z
dc.date.available2020-09-08T21:41:52Z
dc.date.issued2020-08
dc.date.submitted2020-03
dc.identifier.issn1743-0003
dc.identifier.urihttps://hdl.handle.net/1721.1/127203
dc.description.abstractBackground: Functional magnetic resonance imaging (fMRI) provides critical information about the neurophysiology of the central nervous systems (CNS), posing clinical significance for the understanding of neuropathologies and advancement of rehabilitation. Typical fMRI study designs include subjects performing designed motor tasks within specific time frames, in which fMRI data are then analyzed by assuming that observed functional brain activations correspond to the designed tasks. Therefore, developing MRI-compatible sensors that enable real-time monitoring of subjects’ task performances would allow for highly accurate fMRI studies. While several MRI-compatible sensors have been developed, none have demonstrated the ability to measure individual muscle fascicle length during fMRI, which could help uncover the complexities of the peripheral and central nervous systems. Furthermore, previous MRI-compatible sensors have been focused on biologically intact populations, limiting accessibility to populations such as those who have undergone amputation. Methods: We propose a lightweight, low-cost, skin impedance-insensitive pressure-based muscular motion sensor (pMMS) that provides reliable estimates of muscle fascicle length and joint angle. The muscular motions are captured through measured pressure changes in an air pocket wrapped around the muscle of interest, corresponding to its muscular motion. The muscle fascicle length and joint angle are then estimated from the measured pressure changes based on the proposed muscle-skin-sensor interaction dynamics. Furthermore, we explore an integration method of multiple pMMS systems to expand the sensor capacity of estimating muscle fascicle length and joint angle. Ultrasound imaging paired with joint encoder measurements are utilized to assess pMMS estimation accuracy of muscle fascicle length in the tibialis anterior (TA) and ankle joint angle, respectively, of five biologically intact subjects. Results: We found that a single pMMS sufficiently provides robust and accurate estimations of TA muscle fascicle length and ankle joint angle during dorsiflexion at various speeds and amplitudes. Further, differential pressure readings from two pMMSs, in which each pMMS were proximally and distally placed, were able to mitigate errors due to perturbations, expanding pMMS capacity for muscle fascicle length and ankle joint angle estimation during the full range of plantar flexion and dorsiflexion. Conclusions: Our results from this study demonstrate the feasibility of the pMMS system to further be incorporated in fMRI settings for real-time monitoring of subjects’ task performances, allowing sophisticated fMRI study designs.en_US
dc.publisherSpringer Science and Business Media LLCen_US
dc.relation.isversionofhttp://dx.doi.org/10.1186/s12984-020-00745-8en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceBioMed Centralen_US
dc.titlePressure based MRI-compatible muscle fascicle length and joint angle estimationen_US
dc.typeArticleen_US
dc.identifier.citationSong, Hyungeun et al. "Pressure based MRI-compatible muscle fascicle length and joint angle estimation." Journal of NeuroEngineering and Rehabilitation 17, 1 (August 2020): 118 © 2020 Springer Natureen_US
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratoryen_US
dc.contributor.departmentProgram in Media Arts and Sciences (Massachusetts Institute of Technology)en_US
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technologyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Medical Engineering & Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Center for Extreme Bionicsen_US
dc.relation.journalJournal of NeuroEngineering and Rehabilitationen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-08-30T03:25:11Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dspace.date.submission2020-08-30T03:25:10Z
mit.journal.volume17en_US
mit.journal.issue1en_US
mit.licensePUBLISHER_CC
mit.metadata.statusComplete


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