The cloudUPDRS app: a medical device for the clinical assessment of Parkinson's Disease
- Submitting institution
-
Birkbeck College
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 194
- Type
- D - Journal article
- DOI
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10.1016/j.pmcj.2017.12.005
- Title of journal
- Pervasive and Mobile Computing
- Article number
- -
- First page
- 146
- Volume
- 43
- Issue
- -
- ISSN
- 1574-1192
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2017
- URL
-
http://eprints.bbk.ac.uk/id/eprint/20640/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
11
- Research group(s)
-
2 - Experimental Data Science
- Citation count
- 8
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents core computational methods developed for cloudUPDRS, the first smartphone app certified as a Class I medical device for the assessment of Parkinson’s disease. cloudUPDRS is an interdisciplinary collaboration between computer scientists, neuroscientists and social scientists enabled by distinct computational innovations: a deep learning model that ensures high quality measurements; a machine learning method for test personalisation; and a sensor data-processing pipeline. cloudUPDRS currently supports several international clinical studies. cloudUPDRS methods are available in the open source PDkit toolbox (funded by MJFox Foundation), downloaded more than 30,000 times from the official python module repository PyPI.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -