Stamate, C;
Magoulas, GD;
Kueppers, S;
Nomikou, E;
Daskalopoulos, I;
Luchini, MU;
Moussouri, T;
(2017)
Deep Learning Parkinson's from Smartphone Data.
In:
(Proceedings) IEEE International Conference on Pervasive Computing and Communications (PerCom).
IEEE
Preview |
Text
Parkinson.pdf - Published Version Download (464kB) | Preview |
Abstract
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.
| Type: | Proceedings paper |
|---|---|
| Title: | Deep Learning Parkinson's from Smartphone Data |
| Event: | IEEE International Conference on Pervasive Computing and Communications (PerCom) |
| Location: | HI |
| Dates: | 13 March 2017 - 17 March 2017 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/PERCOM.2017.7917848 |
| Publisher version: | https://doi.org/10.1109/PERCOM.2017.7917848 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Computer Science, Information Systems, Computer Science, Theory & Methods, Telecommunications, Computer Science, DISEASE RATING-SCALE, NEURAL-NETWORKS, VALIDATION, MACHINE, MOTOR, GAIT |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Institute of Archaeology UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Institute of Archaeology > Institute of Archaeology Gordon Square |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10060778 |
Archive Staff Only
 |
View Item |
