Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Anti-CRISPR-Based and CRISPR-Based Genome Editing of <i>Sulfolobus islandicus</i> Rod-Shaped Virus 2.

Viruses·2018
Same author

Dissolution and homogeneous photocatalysis of polymeric carbon nitride.

Chemical science·2018
Same author

Mycophenolic Acid as a Promising Fungal Dimorphism Inhibitor to Control Sugar Cane Disease Caused by Sporisorium scitamineum.

Journal of agricultural and food chemistry·2018
Same author

A smart tumor microenvironment responsive nanoplatform based on upconversion nanoparticles for efficient multimodal imaging guided therapy.

Biomaterials science·2018
Same author

Understanding Membrane Protein Drug Targets in Computational Perspective.

Current drug targets·2018
Same author

The important role of apolipoprotein A-II in ezetimibe driven reduction of high cholesterol diet-induced atherosclerosis.

Atherosclerosis·2018

Related Experiment Video

Updated: Sep 4, 2025

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol
13:09

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

Published on: April 1, 2018

10.4K

A Robust Movement Quantification Algorithm of Hyperactivity Detection for ADHD Children Based on 3D Depth Images.

Ling He, Fei He, Yuanyuan Li

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 13, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an objective system using depth imaging to automatically quantify hyperactivity in children with Attention Deficit Hyperactivity Disorder (ADHD). The system accurately detects and measures movements, aligning with clinical observations.

    More Related Videos

    Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
    07:09

    Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

    Published on: November 14, 2018

    10.8K
    An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor
    07:25

    An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

    Published on: February 12, 2018

    7.0K

    Related Experiment Videos

    Last Updated: Sep 4, 2025

    Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol
    13:09

    Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

    Published on: April 1, 2018

    10.4K
    Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
    07:09

    Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

    Published on: November 14, 2018

    10.8K
    An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor
    07:25

    An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

    Published on: February 12, 2018

    7.0K

    Area of Science:

    • Computer Science
    • Medical Imaging
    • Pediatrics

    Background:

    • Attention Deficit Hyperactivity Disorder (ADHD) is a common childhood disorder characterized by hyperactivity.
    • Current ADHD diagnosis relies on subjective clinical assessments and rating scales.
    • Objective quantification of hyperactivity in children is needed.

    Purpose of the Study:

    • To develop an objective system for automatically quantifying movements in children with ADHD.
    • To introduce a novel method for movement detection and quantification using depth images.
    • To validate the system's performance against clinical observations.

    Main Methods:

    • Utilized depth images for movement analysis.
    • Developed a salient object extraction method for body segmentation.
    • Implemented a local search algorithm with new evaluation metrics for motion detection.
    • Investigated two parameters for quantifying movement participation and body part displacement.

    Main Results:

    • Movement detection accuracy ranged from 91.0% to 95.0% on a dataset of children with ADHD.
    • Movement quantification results demonstrated consistency with clinical observations.
    • The system's performance was further validated using the public MSR Action 3D dataset.

    Conclusions:

    • The proposed objective system effectively quantifies movements in children with ADHD.
    • Depth image analysis offers a promising approach for objective ADHD symptom assessment.
    • This technology has the potential to enhance the diagnostic process for ADHD.