Show simple item record

dc.contributor.authorMoretti, Caio B.
dc.contributor.authorHamilton, Taya
dc.contributor.authorEdwards, Dylan J.
dc.contributor.authorPeltz, Avrielle R.
dc.contributor.authorChang, Johanna L.
dc.contributor.authorCortes, Mar
dc.contributor.authorDelbe, Alexandre C. B.
dc.contributor.authorVolpe, Bruce T.
dc.contributor.authorKrebs, Hermano I.
dc.date.accessioned2022-01-03T17:12:50Z
dc.date.available2022-01-03T17:12:50Z
dc.date.issued2021-12-29
dc.identifier.issn332-8886
dc.identifier.urihttps://hdl.handle.net/1721.1/138774
dc.description.abstractAbstract Background A detailed sensorimotor evaluation is essential in planning effective, individualized therapy post-stroke. Robotic kinematic assay may offer better accuracy and resolution to understand stroke recovery. Here we investigate the added value of distal wrist measurement to a proximal robotic kinematic assay to improve its correlation with clinical upper extremity measures in chronic stroke. Secondly, we compare linear and nonlinear regression models. Methods Data was sourced from a multicenter randomized controlled trial conducted from 2012 to 2016, investigating the combined effect of robotic therapy and transcranial direct current stimulation (tDCS). 24 kinematic metrics were derived from 4 shoulder-elbow tasks and 35 metrics from 3 wrist and forearm evaluation tasks. A correlation-based feature selection was performed, keeping only features substantially correlated with the target attribute (R > 0.5.) Nonlinear models took the form of a multilayer perceptron neural network: one hidden layer and one linear output. Results Shoulder-elbow metrics showed a significant correlation with the Fugl Meyer Assessment (upper extremity, FMA-UE), with a R = 0.82 (P < 0.001) for the linear model and R = 0.88 (P < 0.001) for the nonlinear model. Similarly, a high correlation was found for wrist kinematics and the FMA-UE (R = 0.91 (P < 0.001) and R = 0.92 (P < 0.001) for the linear and nonlinear model respectively). The combined analysis produced a correlation of R = 0.91 (P < 0.001) for the linear model and R = 0.91 (P < 0.001) for the nonlinear model. Conclusions Distal wrist kinematics were highly correlated to clinical outcomes, warranting future investigation to explore our nonlinear wrist model with acute or subacute stroke populations. Trial registration http://www.clinicaltrials.gov . Actual study start date September 2012. First registered on 15 November 2012. Retrospectively registered. Unique identifiers: NCT01726673 and NCT03562663 .en_US
dc.publisherBioMed Centralen_US
dc.relation.isversionofhttps://doi.org/10.1186/s42234-021-00082-8en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceBioMed Centralen_US
dc.titleRobotic Kinematic measures of the arm in chronic Stroke: part 2 – strong correlation with clinical outcome measuresen_US
dc.typeArticleen_US
dc.identifier.citationBioelectronic Medicine. 2021 Dec 29;7(1):21en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.relation.journalBioelectronic Medicineen_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.updated2022-01-02T04:10:32Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dspace.date.submission2022-01-02T04:10:32Z
mit.journal.volume7en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work Neededen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record