Abstract
The CloudUPDRS app has been developed as a Class I medical device to assess the severity of motor symptoms for Parkinson’s Disease using a fully automated data capture and signal analysis process based on the standard Unified Parkinson’s Disease Rating Scale. In this paper we report on the design and development of the signal processing 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.
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CITATION STYLE
Fragopanagos, N. F., Kueppers, S., Kassavetis, P., Luchini, M. U., & Roussos, G. (2017). Towards longitudinal data analytics in Parkinson’s disease. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (Vol. 181 LNICST, pp. 56–61). Springer Verlag. https://doi.org/10.1007/978-3-319-49655-9_9
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