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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

1
Telematics Engineering Department, UC3M-BS Institute of Financial Big Data, Universidad Carlos III de Madrid, Av. Universidad, 28911 Leganes, Spain
2
School of Health and Related Research, University of Sheffield, Regent Court, S1 4DA Sheffield, UK
3
Centre for Sports Engineering Research, Sheffield Hallam University, S10 2LW Sheffield, UK
4
Ryegate Children’s Centre, S10 5DD Sheffield, UK
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(13), 2935; https://doi.org/10.3390/s19132935
Received: 5 June 2019 / Revised: 28 June 2019 / Accepted: 1 July 2019 / Published: 3 July 2019
(This article belongs to the Special Issue Inertial Sensors for Activity Recognition and Classification)
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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. View Full-Text
Keywords: ADHD; tri-axial accelerometers; deep learning; Recurrent Neural Networks (RNN); Long Short Term Memory (LSTM) ADHD; tri-axial accelerometers; deep learning; Recurrent Neural Networks (RNN); Long Short Term Memory (LSTM)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Muñoz-Organero, M.; Powell, L.; Heller, B.; Harpin, V.; Parker, J. Using Recurrent Neural Networks to Compare Movement Patterns in ADHD and Normally Developing Children Based on Acceleration Signals from the Wrist and Ankle. Sensors 2019, 19, 2935.

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