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    Deep learning Parkinson's from smartphone data

    Stamate, Cosmin and Magoulas, George D. and Kueppers, S. and Nomikou, E. and Daskalopoulos, I. and Luchini, M.U. and Moussouri, T. and Roussos, George (2017) Deep learning Parkinson's from smartphone data. In: UNSPECIFIED (ed.) 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE Computer Society, pp. 31-40. ISBN 9781509043279.

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    The cloudUPDRS app is a Class I medical device, namely an active transient non-invasive instrument, certified by the Medicines and Healthcare products Regulatory Agency in the UK for the clinical assessment of the motor symptoms of Parkinson's Disease. The app follows closely 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; and, requires the user to perform a sequence of iterated movements which are recorded by the phone sensors. This paper discusses how 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 minutes typically required by an experienced patient, to below 4 minutes, 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 used to identify failures to follow the movement protocol while at the same time limiting false positives to avoid unnecessary repetition. We address the latter by developing a machine learning approach to personalise assessments by selecting those elements of the UPDRS protocol that most closely match individual symptom profiles and thus offer the highest inferential power hence closely estimating the patent's overall UPRDS score.


    Item Type: Book Section
    Additional Information: ISSN: 2474-249X
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Administrator
    Date Deposited: 29 Jun 2017 09:41
    Last Modified: 09 Aug 2023 12:42


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