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dc.contributor.authorMunoz-Organero, Mario
dc.contributor.authorPowell, Lauren
dc.contributor.authorHeller, Ben
dc.contributor.authorHarpin, Val
dc.contributor.authorParker, Jack
dc.date.accessioned2020-04-01T10:30:35Z
dc.date.available2020-04-01T10:30:35Z
dc.date.issued2019-07-03
dc.identifier.citationMuñoz-Organero, M., Powell, L., Heller, B., Harpin, V. and Parker, J., (2019). 'Using recurrent neural networks to compare movement patterns in ADHD and normally developing children based on acceleration signals from the wrist and ankle'. Sensors, 19(13), pp. 1-17.en_US
dc.identifier.doi10.3390/s19132935
dc.identifier.urihttp://hdl.handle.net/10545/624639
dc.description.abstractAttention deficit and hyperactivity disorder (ADHD) is a neurodevelopmental condition that affects, among other things, the movement patterns of children suffering it. Inattention, hyperactivity and impulsive behaviors, major symptoms characterizing ADHD, result not only in differences in the activity levels but also in the activity patterns themselves. This paper proposes and trains a Recurrent Neural Network (RNN) to characterize the moment patterns for normally developing children and uses the trained RNN in order to assess differences in the movement patterns from children with ADHD. Each child is monitored for 24 consecutive hours, in a normal school day, wearing 4 tri-axial accelerometers (one at each wrist and ankle). The results for both medicated and non-medicated children with ADHD, and for different activity levels are presented. While the movement patterns for non-medicated ADHD diagnosed participants showed higher differences as compared to those of normally developing participants, those differences were only statistically significant for medium intensity movements. On the other hand, the medicated ADHD participants showed statistically different behavior for low intensity movements.en_US
dc.description.sponsorshipNIHR CLAHRC YHen_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.urlhttps://www.mdpi.com/1424-8220/19/13/2935en_US
dc.relation.urlhttp://shura.shu.ac.uk/id/eprint/24800en_US
dc.relation.urlhttp://eprints.whiterose.ac.uk/148137/en_US
dc.subjectADHDen_US
dc.subjecttri-axial accelerometersen_US
dc.subjectdeep learningen_US
dc.subjectRecurrent Neural Networksen_US
dc.subjectLong Short Term Memoryen_US
dc.titleUsing recurrent neural networks to compare movement patterns in adhd and normally developing children based on acceleration signals from the wrist and ankleen_US
dc.typeArticleen_US
dc.identifier.eissn14248220
dc.contributor.departmentUniversidad Carlos III de Madriden_US
dc.contributor.departmentRyegate Childen's Centre, Sheffield Children's NHS FTen_US
dc.contributor.departmentSheffield Hallam Universityen_US
dc.contributor.departmentUniversity of Sheffielden_US
dc.identifier.journalSensorsen_US
dcterms.dateAccepted2019-07-01
refterms.dateFOA2020-04-01T10:30:36Z
dc.author.detail787041en_US


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