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dc.contributor.authorGhosh, Abhirup
dc.contributor.authorPuthusseryppady, Vaisakh
dc.contributor.authorChan, Dennis
dc.contributor.authorMascolo, Cecilia
dc.contributor.authorHornberger, Michael
dc.date.accessioned2022-02-24T16:05:19Z
dc.date.available2022-02-24T16:05:19Z
dc.date.issued2022-02-24
dc.date.submitted2021-11-17
dc.identifier.issn2045-2322
dc.identifier.others41598-022-06899-w
dc.identifier.other6899
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334417
dc.description.abstractImpairment of navigation is one of the earliest symptoms of Alzheimer's disease (AD), but to date studies have involved proxy tests of navigation rather than studies of real life behaviour. Here we use GPS tracking to measure ecological outdoor behaviour in AD. The aim was to use data-driven machine learning approaches to explore spatial metrics within real life navigational traces that discriminate AD patients from controls. 15 AD patients and 18 controls underwent tracking of their outdoor navigation over two weeks. Three kinds of spatiotemporal features of segments were extracted, characterising the mobility domain (entropy, segment similarity, distance from home), spatial shape (total turning angle, segment complexity), and temporal characteristics (stop duration). Patients significantly differed from controls on entropy (p-value 0.008), segment similarity (p-value [Formula: see text]), and distance from home (p-value [Formula: see text]). Graph-based analyses yielded preliminary data indicating that topological features assessing the connectivity of visited locations may also differentiate patients from controls. In conclusion, our results show that specific outdoor navigation features discriminate AD patients from controls, which has significant implication for future AD diagnostics, outcome measures and interventions. Furthermore, this work illustrates how wearables-based sensing of everyday behaviour may be used to deliver ecologically-valid digital biomarkers of AD pathophysiology.
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.subjectArticle
dc.subject/692/53/2421
dc.subject/631/378/1689/1283
dc.subject/639/705/1046
dc.subjectarticle
dc.titleMachine learning detects altered spatial navigation features in outdoor behaviour of Alzheimer's disease patients.
dc.typeArticle
dc.date.updated2022-02-24T16:05:19Z
prism.issueIdentifier1
prism.publicationNameSci Rep
prism.volume12
dc.identifier.doi10.17863/CAM.81832
dcterms.dateAccepted2022-01-31
rioxxterms.versionofrecord10.1038/s41598-022-06899-w
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.identifier.eissn2045-2322
pubs.funder-project-idWellcome Trust (213939)
pubs.funder-project-idthe Earle & Stuart Charitable Trust and the Faculty of Medicine and Health Sciences, University of East Anglia (R205319)
cam.issuedOnline2022-02-24


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