Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling
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 diarie...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-09-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fncom.2018.00072/full |
| _version_ | 1819100387693559808 |
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| author | Ritesh A. Ramdhani Anahita Khojandi Oleg Shylo Brian H. Kopell Brian H. Kopell Brian H. Kopell Brian H. Kopell |
| author_facet | Ritesh A. Ramdhani Anahita Khojandi Oleg Shylo Brian H. Kopell Brian H. Kopell Brian H. Kopell Brian H. Kopell |
| author_sort | Ritesh A. Ramdhani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T01:01:58Z |
| format | Article |
| id | doaj.art-6f0ec13f04514a749d129ee21e2d8e16 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-22T01:01:58Z |
| publishDate | 2018-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-6f0ec13f04514a749d129ee21e2d8e162022-12-21T18:44:11ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882018-09-011210.3389/fncom.2018.00072326741Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven ModelingRitesh A. Ramdhani0Anahita Khojandi1Oleg Shylo2Brian H. Kopell3Brian H. Kopell4Brian H. Kopell5Brian H. Kopell6Department of Neurology, School of Medicine, New York University, New York City, NY, United StatesDepartment of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN, United StatesDepartment of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN, United StatesDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York City, NY, United StatesDepartment of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York City, NY, United StatesDepartment of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, United StatesDepartment of Neuroscience, Icahn School of Medicine at Mount Sinai, New York City, NY, United StatesThe 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.https://www.frontiersin.org/article/10.3389/fncom.2018.00072/fullwearable sensorsParkinson's diseasemachine learningaccelerometergyroscope |
| spellingShingle | Ritesh A. Ramdhani Anahita Khojandi Oleg Shylo Brian H. Kopell Brian H. Kopell Brian H. Kopell Brian H. Kopell Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling Frontiers in Computational Neuroscience wearable sensors Parkinson's disease machine learning accelerometer gyroscope |
| title | Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling |
| title_full | Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling |
| title_fullStr | Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling |
| title_full_unstemmed | Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling |
| title_short | Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling |
| title_sort | optimizing clinical assessments in parkinson s disease through the use of wearable sensors and data driven modeling |
| topic | wearable sensors Parkinson's disease machine learning accelerometer gyroscope |
| url | https://www.frontiersin.org/article/10.3389/fncom.2018.00072/full |
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