Wearable-enabled algorithms for the estimation of parkinson’s symptoms evaluated in a continuous home monitoring setting using inertial sensors

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dc.contributor.authorCrowe, Columen
dc.contributor.authorSica, Marcoen
dc.contributor.authorKenny, Lornaen
dc.contributor.authorO'Flynn, Brendanen
dc.contributor.authorScott Mueller, Daviden
dc.contributor.authorTimmons, Suzanneen
dc.contributor.authorBarton, Johnen
dc.contributor.authorTedesco, Salvatoreen
dc.contributor.funderEnterprise Irelanden
dc.contributor.funderAbbVieen
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2025-01-08T17:21:03Z
dc.date.available2025-01-08T17:21:03Z
dc.date.issued2024en
dc.description.abstractMotor symptoms such as tremor and bradykinesia can develop concurrently in Parkinson’s disease; thus, the ideal home monitoring system should be capable of tracking symptoms continuously despite background noise from daily activities. The goal of this study is to demonstrate the feasibility of detecting symptom episodes in a free-living scenario, providing a higher level of interpretability to aid AI-powered decision-making. Machine learning models trained on wearable sensor data from scripted activities performed by participants in the lab and clinician ratings of the video recordings of these tasks identified tremor, bradykinesia, and dyskinesia in the supervised lab environment with a balanced accuracy of 83%, 75%, and 81%, respectively, when compared to the clinician ratings. The performance of the same models when evaluated on data from subjects performing unscripted activities unsupervised in their own homes achieved a balanced accuracy of 63%, 63%, and 67%, respectively, in comparison to self-assessment patient diaries, further highlighting their limitations. The ankle-worn sensor was found to be advantageous for the detection of dyskinesias but did not show an added benefit for tremor and bradykinesia detection here.en
dc.description.sponsorshipEnterprise Ireland (EI), AbbVie, Inc. (Grant Number: IP 2017 0625)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationCrowe, C., Sica, M., Kenny, L., O’Flynn, B., Scott Mueller, D., Timmons, S., Barton, J. and Tedesco, S. (2024) ‘Wearable-enabled algorithms for the estimation of parkinson’s symptoms evaluated in a continuous home monitoring setting using inertial sensors’, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 32, pp. 3828–3836. https://doi.org/10.1109/TNSRE.2024.3477003en
dc.identifier.doihttps://doi.org/10.1109/TNSRE.2024.3477003en
dc.identifier.eissn1558-0210en
dc.identifier.endpage3836en
dc.identifier.issn1534-4320en
dc.identifier.journaltitleIEEE Transactions on Neural Systems and Rehabilitation Engineeringen
dc.identifier.other39383074en
dc.identifier.startpage3828en
dc.identifier.urihttps://hdl.handle.net/10468/16797
dc.identifier.volume32en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Maternity/Adoptive Leave Allowance/12/RC/2289-P2s/IE/INSIGHT Phase 2/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/en
dc.rights© 2024, The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectMonitoringen
dc.subjectMotorsen
dc.subjectWristen
dc.subjectDiseasesen
dc.subjectBiomedical monitoringen
dc.subjectAnkleen
dc.subjectAccuracyen
dc.subjectData modelsen
dc.subjectData collectionen
dc.subjectWearable devicesen
dc.titleWearable-enabled algorithms for the estimation of parkinson’s symptoms evaluated in a continuous home monitoring setting using inertial sensorsen
dc.typeArticle (peer-reviewed)en
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