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

Force Classification01:22

Force Classification

1.0K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.0K
Dementia01:30

Dementia

65
Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual....
65
Aggregates Classification01:29

Aggregates Classification

289
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
289

You might also read

Related Articles

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

Sort by
Same author

Associations among sleep quality, cognitive decline, and Alzheimer's disease pathology in older adults: A longitudinal study.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Combining plasma biomarkers and cognitive challenge tests enhances prediction of functional trajectories of decline among older adults with cognitive impairment.

Journal of Alzheimer's disease : JAD·2026
Same author

Human-Centered Multi-Sensor Framework for Identifying Driving Patterns Associated with Cognitive Decline Through Quantitative Analysis.

Research square·2026
Same author

Development of a culturally tailored medical nutrition therapy to improve dietary adherence in type 2 diabetes in Benin: an ORBIT model-based protocol.

Frontiers in nutrition·2026
Same author

Abnormal Driving Pattern Detection from GPS Trajectories Using Vision Transformer.

Research square·2026
Same author

Radioguided occult lesion localisation for wide local excision, excision biopsies and in combination with radioisotope sentinel lymph node localisation (SNOLL) - 10 year experience of a single centre.

Surgery in practice and science·2026
Same journal

A Multi-Head Attention Transformer Model for Wearable in Situ Fall Detection.

IEEE access : practical innovations, open solutions·2026
Same journal

Validating Single-Camera Pose Estimation Against Multi-Camera Motion Capture for Accessible Biomechanical Assessment.

IEEE access : practical innovations, open solutions·2026
Same journal

Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification.

IEEE access : practical innovations, open solutions·2026
Same journal

Radio-Frequency Toroid Susceptometry of Magnetic Nanoparticles: What Goes Around Comes Around.

IEEE access : practical innovations, open solutions·2026
Same journal

Cross-Architecture Knowledge Distillation for Histopathological Image Analysis.

IEEE access : practical innovations, open solutions·2026
Same journal

Mislabel Identification Using Transfer Learning-Based Ensemble Method.

IEEE access : practical innovations, open solutions·2026
See all related articles

Related Experiment Video

Updated: May 10, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.4K

Quad-tree Based Driver Classification using Deep Learning for Mild Cognitive Impairment Detection.

Seyedeh Gol Ara Ghoreishi1, Charles Boateng1, Sonia Moshfeghi1

  • 1Florida Atlantic University, Boca Raton, USA.

IEEE Access : Practical Innovations, Open Solutions
|April 21, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel quad-tree approach for driver classification to detect Mild Cognitive Impairment (MCI). The method effectively analyzes driving patterns, achieving high accuracy for improved road safety and cognitive health monitoring.

Keywords:
Convolutional Neural NetworksGPS dataSpatiotemporal datadriving behaviorolder driver classificationquad-tree decompositiontrajectory analysis

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

901
Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients
07:42

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

Published on: December 16, 2022

2.8K

Related Experiment Videos

Last Updated: May 10, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.4K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

901
Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients
07:42

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

Published on: December 16, 2022

2.8K

Area of Science:

  • Computational neuroscience
  • Transportation engineering
  • Machine learning for healthcare

Background:

  • Detecting Mild Cognitive Impairment (MCI) is crucial for timely intervention and patient care.
  • Analyzing driving patterns offers a non-invasive method for cognitive health assessment.
  • Existing driver classification methods face challenges with large, complex GPS trajectory data.

Purpose of the Study:

  • To develop an effective method for classifying drivers with Mild Cognitive Impairment (MCI) using GPS data.
  • To propose a novel geo-regional quad-tree structure for analyzing spatial driving patterns.
  • To enhance driver classification accuracy through advanced feature representation and deep learning.

Main Methods:

  • Utilized a real-world dataset of GPS points on a transportation network.
  • Developed a geo-regional quad-tree structure to represent the spatial hierarchy of driving trajectories.
  • Engineered new driving features for input into a Convolutional Neural Network (CNN).
  • Implemented a quad-tree based driver classification (QBDC) algorithm.

Main Results:

  • The proposed quad-tree based driver classification (QBDC) algorithm achieved a 95% F1 score.
  • Demonstrated significant performance improvement over baseline models.
  • Validated the effectiveness of geo-regional quad-trees in extracting interpretable features from driving patterns.

Conclusions:

  • Geo-regional quad-tree structures are effective for describing complex driving patterns and classifying drivers.
  • The proposed approach shows significant potential for improving road safety and cognitive health monitoring.
  • This method offers a promising avenue for early detection of cognitive decline through driving behavior analysis.