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Computer Science > Machine Learning

arXiv:2205.05361 (cs)
[Submitted on 11 May 2022]

Title:Reducing a complex two-sided smartwatch examination for Parkinson's Disease to an efficient one-sided examination preserving machine learning accuracy

Authors:Alexander Brenner, Michael Fujarski, Tobias Warnecke, Julian Varghese
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Abstract:Sensors from smart consumer devices have demonstrated high potential to serve as digital biomarkers in the identification of movement disorders in recent years. With the usage of broadly available smartwatches we have recorded participants performing technology-based assessments in a prospective study to research Parkinson's Disease (PD). In total, 504 participants, including PD patients, differential diagnoses (DD) and healthy controls (HC), were captured with a comprehensive system utilizing two smartwatches and two smartphones. To the best of our knowledge, this study provided the largest PD sample size of two-hand synchronous smartwatch measurements. To establish a future easy-to use home-based assessment system in PD screening, we systematically evaluated the performance of the system based on a significantly reduced set of assessments with only one-sided measures and assessed, whether we can maintain classification accuracy.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2205.05361 [cs.LG]
  (or arXiv:2205.05361v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2205.05361

Submission history

From: Alexander Brenner [view email]
[v1] Wed, 11 May 2022 09:12:59 UTC (1,324 KB)
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