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 Neuroscijournal_title
Frontiers in computational neuroscienceauthors
Ramdhani RA,Khojandi A,Shylo O,Kopell BHdoi
10.3389/fncom.2018.00072subject
Has Abstractpub_date
2018-09-11 00:00:00pages
72issn
1662-5188journal_volume
12pub_type
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