Evidence map›Paper›PMID 40256415›Full record

ArticleIEEE access : practical innovations, open solutions2025

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

Seyedeh Gol Ara Ghoreishi, Charles Boateng, Sonia Moshfeghi, Muhammad Tanveer Jan, Joshua Conniff, Kwangsoo Yang, Jinwoo Jang, Borko Furht, David Newman, Ruth Tappen and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in IEEE access : practical innovations, open solutions, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Seyedeh Gol Ara GhoreishiFlorida Atlantic University, Boca Raton, USA.
Charles BoatengFlorida Atlantic University, Boca Raton, USA.
Sonia MoshfeghiFlorida Atlantic University, Boca Raton, USA.
Muhammad Tanveer JanFlorida Atlantic University, Boca Raton, USA.
Joshua ConniffFlorida Atlantic University, Boca Raton, USA.
Kwangsoo YangFlorida Atlantic University, Boca Raton, USA.
Jinwoo JangFlorida Atlantic University, Boca Raton, USA.
Borko FurhtFlorida Atlantic University, Boca Raton, USA.
David NewmanFlorida Atlantic University, Boca Raton, USA.
Ruth TappenFlorida Atlantic University, Boca Raton, USA.
Monica RosselliFlorida Atlantic University, Boca Raton, USA.
Kelly JacksonFlorida Atlantic University, Boca Raton, USA.

Funding

In-Vehicle Sensors to Detect Cognitive Change in Older DriversR01AG068472 · NIA · FLORIDA ATLANTIC UNIVERSITY · PI NEWMAN, DAVID, TAPPEN, RUTH M · 2020 to 2024
$5.5M
NIA NIH HHS R01 AG068472
6 · The paper itself

Abstract

Given GPS points on a transportation network, the goal of the Quad-tree Based Driver Classification (QBDC) problem is to identify whether drivers have Mild Cognitive Impairment (MCI). The QBDC problem is challenging due to the large volume and complexity of the data. This paper proposes a quad-tree based approach to the QBDC problem by analyzing driving patterns using a real-world dataset. We propose a geo-regional quad-tree structure to capture the spatial hierarchy of driving trajectories and introduce new driving features representation for input into a convolutional neural network (CNN) for driver classification. The experimental results demonstrate the effectiveness of the proposed algorithm, achieving an F1 score of 95% that significantly outperforms the baseline models. These results highlight the potential of geo-regional quad-tree structures to extract interpretable features and describe complex driving patterns. This approach offers significant implications for driver classification, with the potential to improve road safety and cognitive health monitoring.

Indexed as

Convolutional Neural Networksdriving behaviorGPS dataolder driver classificationquad-tree decompositionSpatiotemporal datatrajectory analysis

Identifiers

PMID40256415
PMCPMC12007693

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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.