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Related Experiment Video

Updated: May 13, 2026

Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos

Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos

Published on: December 7, 2018

ADHD identification from real-life recorded video.

Anton Gelashvili¹, Yehezkel S Resheff^2,3, Gaddi Blumrosen^4,5

¹Department of Computer Science, Faculty of sciences, HIT - Holon Institute of Technology, Holon, Israel.

Scientific Reports

|May 11, 2026

Summary

This study introduces a novel AI-powered method for continuous Attention Deficit Hyperactivity Disorder (ADHD) monitoring at home. The system uses video analysis to identify ADHD symptoms, offering potential for improved diagnosis and treatment planning.

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Last Updated: May 13, 2026

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The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

Area of Science:

Neuroscience
Artificial Intelligence
Biomedical Engineering

Background:

Attention Deficit Hyperactivity Disorder (ADHD) affects over 10% of the population, necessitating accurate and early diagnosis.
Current ADHD diagnosis relies on clinical interviews and questionnaires, which are prone to bias, require facility visits, and may miss subtle symptoms.
The dynamic nature of ADHD symptoms and environmental influences pose challenges for traditional diagnostic methods.

Purpose of the Study:

To develop and evaluate a technology for continuous, real-life monitoring of ADHD symptoms in home settings.
To explore the use of deep learning on skeletal joint data for ADHD recognition and scoring.
To identify novel ADHD biomarkers from real-life data for improved understanding and management of the disorder.

Main Methods:

Utilized a dataset of 78 video clips (40 female, 38 male) for analysis.
Employed a pre-trained deep learning framework to extract upper body skeleton joint coordinates over time.
Computed dynamic spatial-temporal features and applied deep learning (TabNet, TCC, LSTM) and general classifiers (XGBoost, Naive Bayes, SVM) for ADHD recognition.

Main Results:

Achieved ADHD recognition accuracy between 0.66-0.88 for females and 0.65-0.82 for males.
Derived real-life ADHD and biomarkers using a contextual clustering method, specific to each gender.
Demonstrated the potential of skeletal joint analysis for ADHD assessment.

Conclusions:

The proposed technology can facilitate continuous ADHD symptom assessment in home environments.
This tool can serve as a supplementary evaluation method for clinicians, complementing existing questionnaires.
The identified biomarkers may enhance the understanding of ADHD progression and aid in personalized treatment planning and medication optimization.