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A Mobile Crowd Sensing Application for Hypertensive Patients

1
Telekom Srbija A.D. Takovska 2, Belgrade 11000, Serbia
2
Endava, Bulevar Milutina Milankovića 11, Belgrade 11000, Serbia
3
Faculty of Technical Sciences, University of Novi Sad, Trg. D. Obradovića 6, Novi Sad 21000, Serbia
4
Svezdrav Rešenja LLC, Đenerala Draže 44, Klenje 15357, Serbia
5
Faculty of Medicine, University of Belgrade, Dr Subotića 8, Belgrade 11000, Serbia
6
Department of Computer Science and Engineering, European University Cyprus, Diogenis Str 6, Nicosia 1516, Cyprus
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(2), 400; https://doi.org/10.3390/s19020400
Received: 29 November 2018 / Revised: 8 January 2019 / Accepted: 11 January 2019 / Published: 19 January 2019
(This article belongs to the Special Issue Realization of Large-Scale Mobile Crowd Sensing Experiments)
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Abstract

Mobile crowd sensing (MCS) is an application that collects data from a network of conscientious volunteers and implements it for the common or personal benefit. This contribution proposes an implementation that collects the data from hypertensive patients, thus creating an experimental database using the cloud service Platform as a Service (PaaS). The challenge is to perform the analysis without the main diagnostic feature for hypertension—the blood pressure. The other problems consider the data reliability in an environment full of artifacts and with limited bandwidth and battery resources. In order to motivate the MCS volunteers, a feedback about the patient’s current status is created, provided by the means of machine-learning (ML) techniques. Two techniques are investigated and the Random Forest algorithm yielded the best results. The proposed platform, with slight modifications, can be adapted to the patients with other cardiovascular problems. View Full-Text
Keywords: mobile crowd sensing; Internet of Everything; hypertension; quality of information; machine learning mobile crowd sensing; Internet of Everything; hypertension; quality of information; machine learning
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Jovanović, S.; Jovanović, M.; Škorić, T.; Jokić, S.; Milovanović, B.; Katzis, K.; Bajić, D. A Mobile Crowd Sensing Application for Hypertensive Patients. Sensors 2019, 19, 400.

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