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Technology-Facilitated Detection of Mild Cognitive Impairment: A Review

  • Cleveland State University
  • Cleveland Clinic Foundation
  • University of Science and Technology Beijing

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Early diagnosis and management of dementia require accurate detection of symptoms and incidents in the pre-dementia stage of mild cognitive impairment (MCI). With the recent development of smart sensing technologies and machine learning algorithms, researchers have started exploring the possibility of automatically detecting symptoms of MCI based on home activity distributions. In this paper, we provide a brief review of the current state of the art in this line of research. We first present an overview of clinical studies on MCI. We then describe various technologies that have been used to collect data regarding patients cognitive levels and behaviors, and methods used to detect patterns and the deviation from these patterns. We also highlight the limitations of the current research work and outline future research tasks, including the development of cheaper and easily portable solutions, as well as personalized tracking technologies.
Original languageEnglish
Title of host publicationIEEE International Conference on Electro Information Technology
Place of Publicationusa
PublisherIEEE Computer [email protected]
Pages284-289
Number of pages6
Volume2018-May
ISBN (Electronic)9781538653982
DOIs
StatePublished - Oct 18 2018
Event2018 IEEE International Conference on Electro/Information Technology, EIT 2018 - Rochester, United States
Duration: May 3 2018May 5 2018

Conference

Conference2018 IEEE International Conference on Electro/Information Technology, EIT 2018
Country/TerritoryUnited States
CityRochester
Period05/3/1805/5/18

Keywords

  • Dementia
  • Event-driven context model
  • Machine learning
  • Mild cognitive impairment
  • Motion tracking
  • Pattern recognition
  • User-object interaction

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