Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling.

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.

journal_name

Front Comput Neurosci

authors

Ramdhani RA,Khojandi A,Shylo O,Kopell BH

doi

10.3389/fncom.2018.00072

subject

Has Abstract

pub_date

2018-09-11 00:00:00

pages

72

issn

1662-5188

journal_volume

12

pub_type

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