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Open AccessArticle

Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform

1
Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany
2
Department of Clinical Psychology and Psychotherapy, Ulm University, 89081 Ulm, Germany
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Department of Speech-Language-Hearing Sciences, Hofstra University, Hempstead, NY 11549, USA
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Clinic and Policlinic for Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany
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Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, 3500 Krems, Austria
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Department of Technical and Business Information Systems, Otto-von-Guericke-University Magdeburg, 39106 Magdeburg, Germany
7
Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany
*
Author to whom correspondence should be addressed.
This paper is an extended version of the conference paper: Kraft, R.; Birk, F.; Reichert, M.; Deshpande, A.; Schlee, W.; Langguth, B.; Baumeister, H.; Probst, T.; Spiliopoulou, M.; Pryss, R. Design and Implementation of a Scalable Crowdsensing Platform for Geospatial Data of Tinnitus Patients. In Proceedings of the 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), Cordoba, Spain, 5–7 June 2019; pp. 294–299.
Sensors 2020, 20(12), 3456; https://doi.org/10.3390/s20123456
Received: 8 May 2020 / Revised: 7 June 2020 / Accepted: 16 June 2020 / Published: 18 June 2020
(This article belongs to the Special Issue Privacy, Trust and Incentives in Crowdsensing)
Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case. View Full-Text
Keywords: mHealth; crowdsensing; tinnitus; geospatial data; cloud-native; stream processing; scalability; architectural design mHealth; crowdsensing; tinnitus; geospatial data; cloud-native; stream processing; scalability; architectural design
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Kraft, R.; Birk, F.; Reichert, M.; Deshpande, A.; Schlee, W.; Langguth, B.; Baumeister, H.; Probst, T.; Spiliopoulou, M.; Pryss, R. Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform. Sensors 2020, 20, 3456.

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