Automatic Extraction and Detection of Characteristic Movement Patterns in Children with ADHD Based on a Convolutional Neural Network (CNN) and Acceleration Images
| Autor: | Mario Munoz-Organero, Ben Heller, Jack Parker, Lauren Powell, Val Harpin |
|---|---|
| Přispěvatelé: | Ministerio de Ciencia, Innovación y Universidades (España) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Male
02 engineering and technology Audiology Wrist lcsh:Chemical technology Accelerometer Biochemistry Convolutional neural network Analytical Chemistry tri-axial accelerometers 0302 clinical medicine Neurodevelopmental disorder Accelerometry 0202 electrical engineering electronic engineering information engineering Prevalence lcsh:TP1-1185 Child Instrumentation Telecomunicaciones Atomic and Molecular Physics and Optics medicine.anatomical_structure Pattern Recognition Physiological Female 020201 artificial intelligence & image processing Convolutional neural networks Psychology CNN medicine.medical_specialty convolutional neural networks (CNN) Adolescent Movement Acceleration Gross motor skill Article 03 medical and health sciences medicine Humans ADHD Electrical and Electronic Engineering Exercise Tri-axial accelerometers business.industry Deep learning deep learning medicine.disease Attention Deficit Disorder with Hyperactivity Neural Networks Computer Artificial intelligence Ankle business Ankle Joint 030217 neurology & neurosurgery |
| Zdroj: | e-Archivo: Repositorio Institucional de la Universidad Carlos III de Madrid Universidad Carlos III de Madrid (UC3M) e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid instname Sensors (Basel, Switzerland) Sensors, Vol 18, Iss 11, p 3924 (2018) Sensors Volume 18 Issue 11 |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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