Citation

Ramdhani RA, Khojandi A, Shylo O, Kopell BH. Front. Comput. Neurosci. 2018; 12: e72.

Affiliation

Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York City, NY, United States.

Copyright

(Copyright © 2018, Frontiers Research Foundation)

DOI

10.3389/fncom.2018.00072

PMID

30254580

PMCID

PMC6141919

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.

Language: en

Keywords

Parkinson's disease; accelerometer; gyroscope; machine learning; wearable sensors