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dc.contributor.authorGoodlich, Benjamin
dc.contributor.authorArmstrong, Ellen L
dc.contributor.authorHoran, Sean A
dc.contributor.authorBaque, Emmah
dc.contributor.authorCarty, Christopher P
dc.contributor.authorAhmadi, Matthew N
dc.contributor.authorTrost, Stewart G
dc.date.accessioned2020-05-25T02:42:10Z
dc.date.available2020-05-25T02:42:10Z
dc.date.issued2020
dc.identifier.issn0012-1622
dc.identifier.doi10.1111/dmcn.14560
dc.identifier.urihttp://hdl.handle.net/10072/394109
dc.description.abstractAIM: To investigate whether activity-monitors and machine learning models could provide accurate information about physical activity performed by children and adolescents with cerebral palsy (CP) who use mobility aids for ambulation. METHOD: Eleven participants (mean age 11y [SD 3y]; six females, five males) classified in Gross Motor Function Classification System (GMFCS) levels III and IV, completed six physical activity trials wearing a tri-axial accelerometer on the wrist, hip, and thigh. Trials included supine rest, upper-limb task, walking, wheelchair propulsion, and cycling. Three supervised learning algorithms (decision tree, support vector machine [SVM], random forest) were trained on features in the raw-acceleration signal. Model-performance was evaluated using leave-one-subject-out cross-validation accuracy. RESULTS: Cross-validation accuracy for the single-placement models ranged from 59% to 79%, with the best performance achieved by the random forest wrist model (79%). Combining features from two or more accelerometer placements significantly improved classification accuracy. The random forest wrist and hip model achieved an overall accuracy of 92%, while the SVM wrist, hip, and thigh model achieved an overall accuracy of 90%. INTERPRETATION: Models trained on features in the raw-acceleration signal may provide accurate recognition of clinically relevant physical activity behaviours in children and adolescents with CP who use mobility aids for ambulation in a controlled setting.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherWiley
dc.relation.ispartofjournalDevelopmental Medicine & Child Neurology
dc.subject.fieldofresearchClinical Sciences
dc.subject.fieldofresearchMedical and Health Sciences
dc.subject.fieldofresearchcode1103
dc.subject.fieldofresearchcode11
dc.titleMachine learning to quantify habitual physical activity in children with cerebral palsy
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationGoodlich, BI; Armstrong, EL; Horan, SA; Baque, E; Carty, CP; Ahmadi, MN; Trost, SG, Machine learning to quantify habitual physical activity in children with cerebral palsy, Developmental Medicine & Child Neurology, 2020
dcterms.dateAccepted2020-04-07
dc.date.updated2020-05-23T03:51:53Z
gro.description.notepublicThis publication was entered as an advanced online version.
gro.hasfulltextNo Full Text
gro.griffith.authorBaque, Emmah
gro.griffith.authorHoran, Sean A.
gro.griffith.authorArmstrong, Ellen L.
gro.griffith.authorGoodlich, Benjamin I.
gro.griffith.authorCarty, Chris P.


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