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    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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    Authors
    Munoz-Organero, Mario
    Powell, Lauren
    Heller, Ben
    Harpin, Val
    Parker, Jack
    Affiliation
    Universidad Carlos III de Madrid
    Ryegate Childen's Centre, Sheffield Children's NHS FT
    Sheffield Hallam University
    University of Sheffield
    Issue Date
    2019-07-03
    
    Metadata
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    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.
    Citation
    Muñoz-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.
    Publisher
    MDPI
    Journal
    Sensors
    URI
    http://hdl.handle.net/10545/624639
    DOI
    10.3390/s19132935
    Additional Links
    https://www.mdpi.com/1424-8220/19/13/2935
    http://shura.shu.ac.uk/id/eprint/24800
    http://eprints.whiterose.ac.uk/148137/
    Type
    Article
    Language
    en
    EISSN
    14248220
    ae974a485f413a2113503eed53cd6c53
    10.3390/s19132935
    Scopus Count
    Collections
    School of Human Sciences

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