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Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson’s Disease Severity

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dc.contributor.authorMorgan, Catherine
dc.contributor.authorMasullo, Alessandro
dc.contributor.authorMirmehdi, Majid
dc.contributor.authorIsotalus, Hanna Kristiina
dc.contributor.authorJovan, Ferdian
dc.contributor.authorMcConville, Ryan
dc.contributor.authorTonkin, Emma L.
dc.contributor.authorWhone, Alan
dc.contributor.authorCraddock, Ian
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.date.accessioned2023-08-28T14:37:01Z
dc.date.available2023-08-28T14:37:01Z
dc.date.issued2023-08-01
dc.descriptionAcknowledgments We gratefully acknowledge the study participants for their time and efforts in participating in this research. We also acknowledge the local Parkinson’s and Other Movement Disorders Health Integration Team (Patient and Public Involvement Group) for their assistance at each step of study design. Funding Sources This work was supported by the SPHERE Next Steps Project funded by the UK Engineering and Physical Sciences Research Council (EPSRC) [Grant EP/R005273/1], the Elizabeth Blackwell Institute for Health Research, and the Wellcome Trust Institutional Strategic Support Fund [Grant code: 204813/Z/16/Z]; by Cure Parkinson’s [Grant code AW021]; and by IXICO [Grant code R101507-101]. Dr. Jonathan de Pass and Mrs. Georgina de Pass made a charitable donation to the University of Bristol through the Development and Alumni Relations Office; the funding pays for the salary of CM, but they have no input into her work.en
dc.description.statusPeer revieweden
dc.format.extent12
dc.format.extent1184958
dc.identifier276189875
dc.identifierf77a698f-954c-44dd-b28b-df7cfdc97b89
dc.identifier85168824556
dc.identifier.citationMorgan, C, Masullo, A, Mirmehdi, M, Isotalus, H K, Jovan, F, McConville, R, Tonkin, E L, Whone, A & Craddock, I 2023, 'Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson’s Disease Severity', Digital Biomarkers, vol. 7, no. 1, pp. 92-103. https://doi.org/10.1159/000530953en
dc.identifier.doi10.1159/000530953
dc.identifier.iss1en
dc.identifier.issn2504-110X
dc.identifier.otherBibtex: 10.1159/000530953
dc.identifier.otherORCID: /0000-0003-4911-540X/work/141300873
dc.identifier.urihttps://hdl.handle.net/2164/21564
dc.identifier.urlhttps://doi.org/10.1159/000530953en
dc.identifier.vol7en
dc.language.isoeng
dc.relation.ispartofDigital Biomarkersen
dc.subjectParkinson's disease-related motor symtomsen
dc.subjectInfluence and/or predict health-related outcomesen
dc.subjectObjective dataen
dc.subjectHome environmenten
dc.subjectMobilityen
dc.subjectVideo Recordingen
dc.subjectR Medicineen
dc.subjectWellcome Trusten
dc.subject204813/Z/16/Zen
dc.subjectEngineering and Physical Sciences Research Council (EPSRC)en
dc.subjectEP/R005273/1en
dc.subject.lccRen
dc.titleAutomated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson’s Disease Severityen
dc.typeJournal articleen

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