Abstract
The article presents the results of research on the problem of using the capabilities of mobile touch phones on the Android Platform for monitoring the status of patients with Parkinson’s disease. The studies were carried out in two directions: interactive and background monitoring. In the interactive mode, the patients during the day can mark the level of their activity, the presence of dyskinesia, their medications taking and other data that cannot be collected without the participation of the patient himself. In the background monitoring, data are collected from mobile phone sensors. Further, the knowledge that will be extracted from this data using data mining techniques will minimize the interactive part of the monitoring. This will make the monitoring process easy for patients and informative for neurologists. In addition to describing the architecture of the system for monitoring the status of patients with Parkinson’s disease using mobile technologies, the article contains examples of the work with the currently created system modules.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bishop, C.: Neural Networks for Pattern Recognition. University Press, Oxford (1995)
Chameau, J.L., Santamarina, J.C.: Membership functions part I: comparing method of measurement. Int. J. Approximate Reasoning 1, 287–301 (1987)
Neural, D.H., Toolbox, N.: For Use with MATLAB / H. Demuth, M. Beale. The MathWorks Inc. (2000)
Levin, O.S., Fedorova, N.V.: Parkinson’s Disease. OAO IPO “Lev Tolstoy”, Moskow (2006). (in Russian)
Medvedev, V.S., Potemkin, V.G.: Neyronnye seti. MATLAB 6. Dialog-MIFI, 496 p. (2002)
Pavlakis, P., Alepis, E., Virvou, M.: Intelligent mobile multimedia application for the support of the elderly. In: Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Piraeus, pp. 297–300 (2012)
Shichkina, Y.A., Degtyarev, A.B., Koblov, A.A., Technology of cleaning and transforming data using the knowledge discovery in databases (KDD) technology for fast application of data mining methods. In: CEUR Workshop Proceedings, vol. 1787, pp. 428–434 (2016)
Stankevich, E., Paramonov, I., Timofeev, I., Demidov, P.G.: Mobile phone sensors in health applications. In: 12th FRUCT Conference, p. 19 (2011)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)
Yoon, B.L.: Artificial neural network technology. ACM SIGICE Bull. 15(3), 3–16 (1989). https://doi.org/10.1145/74657.74658
Acknowledgements
The research was funded by RFBR and CITMA according to the research project #18-57-34001.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Yulia, S., Galina, K., Yulia, I., Elizaveta, S. (2020). The Architecture of the System for Monitoring the Status in Patients with Parkinson’s Disease Using Mobile Technologies. In: Kotenko, I., Badica, C., Desnitsky, V., El Baz, D., Ivanovic, M. (eds) Intelligent Distributed Computing XIII. IDC 2019. Studies in Computational Intelligence, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-32258-8_62
Download citation
DOI: https://doi.org/10.1007/978-3-030-32258-8_62
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32257-1
Online ISBN: 978-3-030-32258-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)