Implementation of a Smartwatch with Machine Learning for Ascertaining Efficacy of Deep Brain Stimulation for Parkinson's Disease Treatment

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F24%3A00381883" target="_blank" >RIV/68407700:21460/24:00381883 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10805665" target="_blank" >https://ieeexplore.ieee.org/document/10805665</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/EHB64556.2024.10805665" target="_blank" >10.1109/EHB64556.2024.10805665</a>

Result language
angličtina
Original language name
Implementation of a Smartwatch with Machine Learning for Ascertaining Efficacy of Deep Brain Stimulation for Parkinson's Disease Treatment
Original language description
The amalgamation of the smartwatch in conjunction with machine learning enables the opportunity to distinguish the efficacy of deep brain stimulation for treating Parkinson's disease. The smartwatch is comprised of an inertial sensor package inclusive of a gyroscope for quantifying the response of deep brain stimulation for a person with Parkinson's disease. The acquired gyroscope signal from the smartwatch can quantify the Parkinson's disease tremor response to prescribed 'On' and 'Off' settings for deep brain stimulation. Through wireless transmission to an email account serving as a provisional Cloud computing resource, the gyroscope signal data can be synthesized to a feature set for machine learning classification. Using a multilayer perceptron neural network, the research successfully demonstrates the ability to attain considerable classification distinction between 'On' and 'Off' settings prescribed to deep brain stimulation for a person with Parkinson's disease using the quantified gyroscope signal data obtained through a smartwatch.
Czech name
—
Czech description
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Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2024 E-Health and Bioengineering Conference (EHB)
ISBN
979-8-3315-3214-7
ISSN
2575-5145
e-ISSN
2575-5145
Number of pages
4
Pages from-to
319-322
Publisher name
IEEE Industrial Electronic Society
Place of publication
Vienna
Event location
Iasi
Event date
Nov 14, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001413708800078

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