Optimizing clinical assessments in Parkinson’s disease through the use of wearable sensors and data driven modeling

Ritesh A. Ramdhani, Anahita Khojandi, Oleg Shylo, Brian H. Kopell

Research output: Contribution to journalReview articlepeer-review

40 Scopus citations

Abstract

The emergence of motion sensors as a tool that provides objective motor performance data on individuals afflicted with Parkinson’s disease offers an opportunity to expand the horizon of clinical care for this neurodegenerative condition. Subjective clinical scales and patient based motor diaries have limited clinometric properties and produce a glimpse rather than continuous real time perspective into motor disability. Furthermore, the expansion of machine learn algorithms is yielding novel classification and probabilistic clinical models that stand to change existing treatment paradigms, refine the application of advance therapeutics, and may facilitate the development and testing of disease modifying agents for this disease. We review the use of inertial sensors and machine learning algorithms in Parkinson’s disease.

Original languageEnglish
Article number72
JournalFrontiers in Computational Neuroscience
Volume12
DOIs
StatePublished - 11 Sep 2018

Keywords

  • Accelerometer
  • Gyroscope
  • Machine learning
  • Parkinson’s disease
  • Wearable sensors

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