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

Prosopagnosia01:24

Prosopagnosia

Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...

You might also read

Related Articles

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

Sort by
Same author

Case Scenarios of People Living with Dementia Who Go Missing: An Education Tool to Manage Risks.

Canadian journal on aging = La revue canadienne du vieillissement·2026
Same author

Characterization of the Term "Wandering" in Dementia-Related Research: A Scoping Review.

Occupational therapy in health care·2026
Same author

Advances in Sensor-Based Technologies for Health, Aging, and Intelligent Care.

Sensors (Basel, Switzerland)·2026
Same author

Mobile applications for time and event management in older adults and their careagivers: A scoping review.

International journal of medical informatics·2026
Same author

Factors Affecting Technology Acceptance and Usability of a Personalized Online Nutrition and Exercise Web-Based App (Heal-Me): A Multimethods Study.

Applied clinical informatics·2026
Same author

Brain-Computer Interface Games for Cognitive Assessment: A Scoping Review.

The Canadian journal of neurological sciences. Le journal canadien des sciences neurologiques·2026

Related Experiment Video

Updated: Jun 13, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.3K

Analyzing Mobility Indicators Using Machine Learning to Detect Mild Cognitive Impairment: A Systematic Scoping

Salamah Alshammari1,2, Munirah Alsubaie1,2, Mathieu Figeys1,3

  • 1Department of Occupational Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, Alberta, Canada.

Applied Clinical Informatics
|September 3, 2025
PubMed
Summary

Technology-based mobility data and machine learning effectively detect mild cognitive impairment (MCI). Wearable devices show promise, but mobile app monitoring for MCI detection requires further research.

More Related Videos

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

7.6K
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

1.2K

Related Experiment Videos

Last Updated: Jun 13, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.3K
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

7.6K
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

1.2K

Area of Science:

  • Gerontology and Cognitive Science
  • Biomedical Engineering and Data Science

Background:

  • Global aging populations are increasing, leading to a higher prevalence of age-related cognitive conditions like mild cognitive impairment (MCI).
  • Early detection of MCI is crucial for timely intervention, yet traditional cognitive assessments have limitations in consistency.
  • Innovative technology-based solutions are needed to address the growing demand for health services related to cognitive decline.

Purpose of the Study:

  • To systematically review how technology-based mobility data collection and machine learning algorithms are utilized for detecting MCI in adults.
  • To identify key mobility indicators and assess the performance of machine learning algorithms in MCI detection based on existing literature.

Main Methods:

  • A systematic scoping review was conducted, searching seven major databases for relevant studies.
  • Inclusion criteria focused on papers analyzing mobility data with machine learning in adults (18+) diagnosed with MCI.
  • Data extraction included mobility indicators, data collection methods, machine learning algorithms, and performance metrics.

Main Results:

  • Twenty-four studies met the criteria, identifying 116 mobility indicators for MCI classification.
  • Wearable devices were the predominant data collection method; mobile applications were least utilized.
  • Machine learning algorithms like logistic regression, random forest, and neural networks achieved a mean accuracy of 86.1%, sensitivity of 84%, and specificity of 72.8%.

Conclusions:

  • Technology-based mobility assessment, particularly using wearable devices and machine learning, shows significant potential for detecting MCI.
  • Key mobility indicators include walking speed, driving patterns (hard braking, night trips, speed), and other movement metrics.
  • Further research into mobile app-based mobility monitoring presents a promising avenue for future MCI detection strategies.