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Related Concept Videos

Attention-Deficit/Hyperactivity Disorder01:30

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Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by persistent inattention, hyperactivity, and impulsivity. It affects approximately 5-8% of children globally, with around 60-70% of cases persisting into adulthood. ADHD has significant implications for educational attainment, social interactions, and occupational success.
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Related Experiment Video

Updated: Aug 15, 2025

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD
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Deep-Learning-Based ADHD Classification Using Children's Skeleton Data Acquired through the ADHD Screening Game.

Wonjun Lee1, Deokwon Lee1, Sanghyub Lee1

  • 1School of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary
This summary is machine-generated.

Early detection of attention deficit hyperactivity disorder (ADHD) is crucial. This study shows that skeleton data from a game accurately identifies ADHD in children with 97.82% accuracy using LSTM algorithms.

Keywords:
ADHDdeep learningscreeningskeleton

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Area of Science:

  • Neuroscience
  • Developmental Psychology
  • Computer Science

Background:

  • Attention deficit hyperactivity disorder (ADHD) is a growing global concern requiring early diagnosis and treatment.
  • Current ADHD diagnosis is time-intensive and lacks simple testing methods.
  • Machine learning is being explored for ADHD screening, but skeleton data has not been utilized.

Purpose of the Study:

  • To investigate the potential of using children's skeleton data for ADHD screening.
  • To develop and validate a game-based system for collecting relevant skeleton data.
  • To differentiate ADHD symptoms like distraction and hyperactivity through movement patterns.

Main Methods:

  • A novel game system was designed to capture children's movements using five Azure Kinect depth sensors.
  • Children's skeleton data was collected during a memory-and-path-following game, including standby and active gameplay phases.
  • Recurrent Neural Network (RNN) algorithms (GRU, RNN, LSTM) with a bidirectional layer and weighted cross-entropy loss were employed for classification.

Main Results:

  • The study achieved a high classification accuracy of 97.82% for ADHD detection.
  • An LSTM algorithm incorporating a bidirectional layer and weighted cross-entropy loss demonstrated superior performance.
  • Skeleton data proved effective in differentiating ADHD-related behavioral symptoms.

Conclusions:

  • Skeleton data derived from a gamified assessment is a viable and highly accurate tool for ADHD screening in children.
  • This approach offers a promising, objective method to aid in the early identification of ADHD.
  • The findings support the integration of motion capture technology and machine learning in clinical diagnostics.