ArticleSensors (Basel, Switzerland)2018
Automatic Extraction and Detection of Characteristic Movement Patterns in Children with ADHD Based on a Convolutional Neural Network (CNN) and Acceleration Images.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Machine Learning Applications for In-School Physical Activity Data Using IMUs in Children and Adolescents: A Systematic Review for Health Promotion.Journal of primary care & community healthPooled it
- Comparison of Fidgeting in Adolescents with Attention-Deficit/Hyperactivity Disorder Between Before and After Stimulant Medication Intake.Journal of child and adolescent psychopharmacology · 2023Trial
- Wearable Technologies for Clinical Assessment and Monitoring in Pediatric Neurodevelopmental Disorders : A Narrative Review.Journal of Korean Neurosurgical Society · 2026Article
- Use of artificial intelligence for outcome assessment in pediatric rehabilitation: a scoping review.Journal of neuroengineering and rehabilitation · 2026Article
- Towards Ecological Validity When Assessing ADHD Symptoms: Patterns in Automatically Collected, Real-World PC Activity Data.International journal of human-computer studies · 2026Article
- Drawing Characteristics Associated with Attention-Deficit/Hyperactivity Disorder in Children: Findings from the Synthetic House-Tree-Person Test.Neuropsychiatric disease and treatment · 2026Article
- The ADVANCE toolkit: Automated descriptive video annotation in naturalistic child environments.Behavior research methods · 2025Article
- ViT-BiLSTM Multimodal Learning for Paediatric ADHD Recognition: Integrating Wearable Sensor Data with Clinical Profiles.Sensors (Basel, Switzerland) · 2025Article
- Temporal Anomaly Detection in Attention-Deficit/Hyperactivity Disorder Using Recurrent Neural Networks.Cureus · 2024Article
- Objective and automatic assessment approach for diagnosing attention-deficit/hyperactivity disorder based on skeleton detection and classification analysis in outpatient videos.Child and adolescent psychiatry and mental health · 2024Article
- Digital assessments for children and adolescents with ADHD: a scoping review.Frontiers in digital health · 2024Article
- ADHD detection based on human action recognition.Neuroscience applied · 2024Article
- Accurate Identification of ADHD among Adults Using Real-Time Activity Data.Brain sciences · 2022Article
- A Wearable Diagnostic Assessment System vs. SNAP-IV for the auxiliary diagnosis of ADHD: a diagnostic test.BMC psychiatry · 2022Article
- Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants.Communications medicine · 2022Article
- Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children.Frontiers in computational neuroscience · 2021Article
- Using Mobile Electroencephalography and Actigraphy to Diagnose Attention-Deficit/Hyperactivity Disorder: Case-Control Comparison Study.JMIR mental health · 2020Article
- Bi-Dimensional Approach Based on Transfer Learning for Alcoholism Pre-disposition Classification via EEG Signals.Frontiers in human neuroscience · 2020Article
- Using Recurrent Neural Networks to Compare Movement Patterns in ADHD and Normally Developing Children Based on Acceleration Signals from the Wrist and Ankle.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
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 (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.