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Publication Detail
The cloudUPDRS app: A medical device for the clinical assessment of Parkinson's Disease
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Article
  • Authors:
    Stamate C, Magoulas GD, Kueppers S, Nomikou E, Daskalopoulos I, Jha A, Pons JS, Rothwell J, Luchini MU, Moussouri T, Iannone M, Roussos G
  • Publication date:
    01/01/2018
  • Pagination:
    146, 166
  • Journal:
    Pervasive and Mobile Computing
  • Volume:
    43
  • Status:
    Published
  • Print ISSN:
    1574-1192
Abstract
© 2017 Elsevier B.V. Parkinson's Disease is a neurological condition distinguished by characteristic motor symptoms including tremor and slowness of movement. To enable the frequent assessment of PD patients, this paper introduces the cloudUPDRS app, a Class I medical device that is an active transient non-invasive instrument, certified by the Medicines and Healthcare products Regulatory Agency in the UK. The app follows closely Part III of the Unified Parkinson's Disease Rating Scale which is the most commonly used protocol in the clinical study of PD; can be used by patients and their carers at home or in the community unsupervised; and, requires the user to perform a sequence of iterated movements which are recorded by the phone sensors. The cloudUPDRS system addresses two key challenges towards meeting essential consistency and efficiency requirements, namely: (i) How to ensure high-quality data collection especially considering the unsupervised nature of the test, in particular, how to achieve firm user adherence to the prescribed movements; and (ii) How to reduce test duration from approximately 25 min typically required by an experienced patient, to below 4 min, a threshold identified as critical to obtain significant improvements in clinical compliance. To address the former, we combine a bespoke design of the user experience tailored so as to constrain context, with a deep learning approach based on Recurrent Convolutional Neural Networks, to identify failures to follow the movement protocol. We address the latter by developing a machine learning approach to personalize assessments by selecting those elements of the test that most closely match individual symptom profiles and thus offer the highest inferential power, hence closely estimating the patent's overall score.
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Clinical and Movement Neurosciences
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