Automatic extraction and detection of characteristic movement patterns in children with adhd based on a convolutional neural network (cnn) and acceleration images

Journal article


Muñoz-Organero, Mario, Powell, Lauren, Heller, Ben, Harpin, Val and Parker, Jack 2018. Automatic extraction and detection of characteristic movement patterns in children with adhd based on a convolutional neural network (cnn) and acceleration images. Sensors. 18 (11), p. 3924. https://doi.org/10.3390/s18113924
AuthorsMuñoz-Organero, Mario, Powell, Lauren, Heller, Ben, Harpin, Val and Parker, Jack
Abstract

Attention deficit and hyperactivity disorder (ADHD) is a neurodevelopmental disorder, which is characterized by inattention, hyperactivity and impulsive behaviors. In particular, children have difficulty keeping still exhibiting increased fine and gross motor activity. This paper focuses on analyzing the data obtained from two tri-axial accelerometers (one on the wrist of the dominant arm and the other on the ankle of the dominant leg) worn during school hours by a group of 22 children (11 children with ADHD and 11 paired controls). Five of the 11 ADHD diagnosed children were not on medication during the study. The children were not explicitly instructed to perform any particular activity but followed a normal session at school alternating classes of little or moderate physical activity with intermediate breaks of more prominent physical activity. The tri-axial acceleration signals were converted into 2D acceleration images and a Convolutional Neural Network (CNN) was trained to recognize the differences between non-medicated ADHD children and their paired controls. The results show that there were statistically significant differences in the way the two groups moved for the wrist accelerometer (t-test p-value <0.05). For the ankle accelerometer statistical significance was only achieved between data from the non-medicated children in the experimental group and the control group. Using a Convolutional Neural Network (CNN) to automatically extract embedded acceleration patterns and provide an objective measure to help in the diagnosis of ADHD, an accuracy of 0.875 for the wrist sensor and an accuracy of 0.9375 for the ankle sensor was achieved.

KeywordsElectrical and Electronic Engineering; Analytical Chemistry; Atomic and Molecular Physics, and Optics; Biochemistry; ADHD
Year2018
JournalSensors
Journal citation18 (11), p. 3924
PublisherMDPI AG
ISSN1424-8220
Digital Object Identifier (DOI)https://doi.org/10.3390/s18113924
Web address (URL)http://hdl.handle.net/10545/624644
https://creativecommons.org/licenses/by/4.0/
hdl:10545/624644
Publication dates14 Nov 2018
Publication process dates
Deposited01 Apr 2020, 15:38
Accepted12 Nov 2018
ContributorsTelematics Engineering Department, Universidad Carlos III de Madrid, Av. Universidad, 30, 28911 Leganes, Spain, University of Sheffield, Sheffield Hallam University and Ryegate Children’s Centre, Sheffield
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