Monitoring Neurological Disorders with AI-enabled Wearable Systems

Charalampos Orfanidis, Adam S. Darwich, Rachel Cheong, Xenofon Fafoutis

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

The age distribution has changed in Europe over the last decade. The group of 45 year-olds and above has increased and the median age in the EU is estimated to increase by 4.5 years during the next 3 decades reaching a median age of approximately 48.2 years according to Eurostat. A similar trend is noticeable in the United States, where the median age increased by 3.3 years from 2000 to 2020 according to Statista. Neurological diseases, such as Huntington disease, have a highly variable onset of 30 - 50 years but they are more prevalent to the older population. One of the first observable physical symptoms is chorea which includes random, uncontrollable and involuntary movements. Internet of Things and wearable systems can assist
long-term monitoring of digital biomarkers such as plantar pressure and gait pattern which are associated with the aforementioned neurological disease. Emerging artificial intelligence models can be utilized to monitor the related digital biomarkers and check if these demonstrate a potential pattern denoting the presence or the development of a neurological disease. Enabling long-term monitoring by utilizing a unobtrusive wearable will increase the
possibilities of early diagnosis, a longer life expectancy, and an improved quality of life for the patient.
Original languageEnglish
Title of host publicationEmerging Devices for Digital Biomarkers
Number of pages5
PublisherACM
Publication date2022
ISBN (Print)978-1-4503-9406-2
DOIs
Publication statusPublished - 2022
EventWorkshop on Emerging Devices for Digital Biomarkers - Portland, United States
Duration: 1 Jul 20221 Jul 2022
https://digibiom.github.io/

Workshop

WorkshopWorkshop on Emerging Devices for Digital Biomarkers
Country/TerritoryUnited States
CityPortland
Period01/07/202201/07/2022
Internet address

Keywords

  • Wearable systems
  • Digital biomarkers
  • Embedded mobile learning
  • Smart health

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