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Cognitive development continues throughout adulthood, undergoing significant shifts across early, middle, and late stages. Individual transition occurs from adolescent idealism to pragmatic and adaptable thinking in early adulthood. During this period, individuals learn to integrate personal beliefs with the recognition that other perspectives are equally valid. Exposure to the complexities of modern society, diverse experiences, and higher education contribute to this adaptive thought process,...
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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.
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Related Experiment Video

Updated: Jun 3, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Smart Driving Technology for Non-Invasive Detection of Age-Related Cognitive Decline.

Peter Serhan1,2,3, Shaun Victor2, Oscar Osorio Perez2,4

  • 1School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85281, USA.

Sensors (Basel, Switzerland)
|January 8, 2025
PubMed
Summary

A new Smart Driving System uses AI and biosensors to detect early signs of Mild Cognitive Impairment (MCI), a precursor to Alzheimer's disease (AD) and related dementias (ADRD). This technology aims for early detection to enable timely lifestyle interventions.

Keywords:
Alzheimer’s Disease (AD)Alzheimer’s early detectionMild Cognitive Impairment (MCI)Smart Driving Systemcognitive health monitoringmulti-modal sensing array (MMS)

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Alzheimer's disease and related dementias (ADRD) pose a significant global health challenge, projected to affect millions.
  • Mild Cognitive Impairment (MCI) is a critical precursor to dementia, offering a window for interventions.
  • Current MCI detection methods are often infrequent and fail to capture subtle cognitive decline.

Purpose of the Study:

  • To introduce a novel, unobtrusive, and economical Smart Driving System for early detection of neurodegenerative diseases.
  • To present the initial development and validation of a system integrating driving performance and biometric data for cognitive assessment.
  • To lay the groundwork for a Smart Driving Device and App for widespread MCI detection.

Main Methods:

  • Development of a multi-modal biosensing array (MMS).
  • Integration of AI algorithms to analyze driving performance and driver's biometrics.
  • Focus on unobtrusive, in-vehicle monitoring for cognitive function insights.

Main Results:

  • The Smart Driving System demonstrates potential for detecting early stages of neurodegenerative diseases through driving and biometric data.
  • The system offers a novel approach to unobtrusive cognitive monitoring.
  • This study represents the first step towards a fully integrated and validated MCI detection system.

Conclusions:

  • The Smart Driving System presents a promising, economical, and unobtrusive method for early MCI detection.
  • Further validation through comprehensive pilot studies is planned to establish its effectiveness.
  • This technology could facilitate timely interventions to delay or prevent dementia progression.