Fragopanagos, N. and Kueppers, S. and Kassavetis, P. and Luchini, M.U. and Roussos, George (2017) Towards longitudinal data analytics in Parkinson's Disease. In: Giokas, K. and Bokor, L. and Hopfg, F. (eds.) eHealth 360°: International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 181. Springer, pp. 56-61. ISBN 9783319496542.
|
Text
updrs_v0.5-iotcare.pdf - Author's Accepted Manuscript Download (410kB) | Preview |
Abstract
The CloudUPDRS app has been developed as a Class I med- ical device to assess the severity of motor symptoms for Parkinson’s Disease using a fully automated data capture and signal analysis pro- cess based on the standard Unified Parkinson’s Disease Rating Scale. In this paper we report on the design and development of the signal pro- cessing and longitudinal data analytics microservices developed to carry out these assessments and to forecast the long-term development of the disease. We also report on early findings from the application of these techniques in the wild with a cohort of early adopters.
Metadata
| Item Type: | Book Section |
|---|---|
| Additional Information: | Series ISSN: 1867-8211. The final publication is available at Springer via the link above. |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Birkbeck Knowledge Lab |
| Depositing User: | George Roussos |
| Date Deposited: | 16 Jan 2017 11:31 |
| Last Modified: | 07 Jul 2024 17:20 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/16629 |
Statistics
Additional statistics are available via IRStats2.
Archive Staff Only (login required)
