Data-driven analysis of parkinson's disease and its detection at an early stage

Aleksandr Talitckii, Anna Anikina, Ekaterina Kovalenko, Oscar Mayora, Venet Osmani, Olga Zimniakova, Maxim Semenov, Ekaterina Bril, Dmitry Dylov, Andrey Somov

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

1 Citation (Scopus)

Abstract

Parkinson's Disease (PD) is the neurological condition caused by the destruction and death of neurons. Nowadays, PD can not be cured and the number of the patients with the PD is continuously growing. In this work, we report a feasibility study involving 74 subjects to whom we proposed 15 exercises helping reveal the risk of PD at an early stage. In this study, we collected the data using wireless wearable sensors and perform the data analysis relying on machine learning techniques. Experimental results demonstrated that the proposed solution tested in real conditions is promising for more complex diagnostic workflow including the assessment of quality of PD therapy.

Original languageEnglish
Title of host publicationProceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2020
PublisherAssociation for Computing Machinery
Pages419-422
Number of pages4
ISBN (Electronic)9781450375320
DOIs
Publication statusPublished - 18 May 2020
Event14th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2020 - Virtual, Online, United States
Duration: 6 Oct 20208 Oct 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference14th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2020
Country/TerritoryUnited States
CityVirtual, Online
Period6/10/208/10/20

Keywords

  • Machine learning
  • Parkinson's disease
  • Wearable sensors

Fingerprint

Dive into the research topics of 'Data-driven analysis of parkinson's disease and its detection at an early stage'. Together they form a unique fingerprint.

Cite this