Using recurrent neural networks to compare movement patterns in ADHD and normally developing children based on acceleration signals from the wrist and ankle

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dc.contributor.author Muñoz Organero, Mario
dc.contributor.author Powell, Lauren
dc.contributor.author Heller, Ben
dc.contributor.author Harpin, Val
dc.contributor.author Parker, Jack
dc.date.accessioned 2020-11-16T16:17:13Z
dc.date.available 2020-11-16T16:17:13Z
dc.date.issued 2019-07-01
dc.identifier.bibliographicCitation Muñoz-Organero, M., Powell, L., Heller, B., Harpin, V., 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), 2935
dc.identifier.issn 1424-8220
dc.identifier.uri http://hdl.handle.net/10016/31425
dc.description.abstract Attention 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
dc.description.sponsorship This research received external funding from the ANALYTICS USING SENSOR DATA FOR FLATCITY project TIN2016-77158-C4-1-R (MINECO/ERDF, EU) funded by the Spanish Agencia Estatal de Investigación (AEI) and the European Regional Development Fund (ERDF).
dc.language.iso eng
dc.publisher MDPI
dc.rights Reconocimiento 3.0 España
dc.rights © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.subject.other ADHD
dc.subject.other Deep learning
dc.subject.other Long short term memory (LSTM)
dc.subject.other Recurrent neural networks (RNN)
dc.subject.other Tri-axial accelerometers
dc.title Using recurrent neural networks to compare movement patterns in ADHD and normally developing children based on acceleration signals from the wrist and ankle
dc.type article
dc.type.review PeerReviewed
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.3390/s19132935
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TIN2016-77158-C4-1-R
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 13, 2935
dc.identifier.publicationlastpage 17
dc.identifier.publicationtitle Sensors
dc.identifier.publicationvolume 19
dc.identifier.uxxi AR/0000024985
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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