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Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform.
Sensors (Basel). 2020 Jun 18; 20(12)S

Abstract

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.

Authors+Show Affiliations

Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany. Department of Clinical Psychology and Psychotherapy, Ulm University, 89081 Ulm, Germany.Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany.Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany.Department of Speech-Language-Hearing Sciences, Hofstra University, Hempstead, NY 11549, USA.Clinic and Policlinic for Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany.Clinic and Policlinic for Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany.Department of Clinical Psychology and Psychotherapy, Ulm University, 89081 Ulm, Germany.Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, 3500 Krems, Austria.Department of Technical and Business Information Systems, Otto-von-Guericke-University Magdeburg, 39106 Magdeburg, Germany.Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32570953

Citation

Kraft, Robin, et al. "Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform." Sensors (Basel, Switzerland), vol. 20, no. 12, 2020.
Kraft R, Birk F, Reichert M, et al. Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform. Sensors (Basel). 2020;20(12).
Kraft, R., Birk, F., Reichert, M., Deshpande, A., Schlee, W., Langguth, B., Baumeister, H., Probst, T., Spiliopoulou, M., & Pryss, R. (2020). Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform. Sensors (Basel, Switzerland), 20(12). https://doi.org/10.3390/s20123456
Kraft R, et al. Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform. Sensors (Basel). 2020 Jun 18;20(12) PubMed PMID: 32570953.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform. AU - Kraft,Robin, AU - Birk,Ferdinand, AU - Reichert,Manfred, AU - Deshpande,Aniruddha, AU - Schlee,Winfried, AU - Langguth,Berthold, AU - Baumeister,Harald, AU - Probst,Thomas, AU - Spiliopoulou,Myra, AU - Pryss,Rüdiger, Y1 - 2020/06/18/ PY - 2020/05/08/received PY - 2020/06/07/revised PY - 2020/06/16/accepted PY - 2020/6/24/entrez PY - 2020/6/24/pubmed PY - 2020/6/24/medline KW - architectural design KW - cloud-native KW - crowdsensing KW - geospatial data KW - mHealth KW - scalability KW - stream processing KW - tinnitus JF - Sensors (Basel, Switzerland) JO - Sensors (Basel) VL - 20 IS - 12 N2 - 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. SN - 1424-8220 UR - https://www.unboundmedicine.com/medline/citation/32570953/Efficient_Processing_of_Geospatial_mHealth_Data_Using_a_Scalable_Crowdsensing_Platform L2 - https://www.mdpi.com/resolver?pii=s20123456 DB - PRIME DP - Unbound Medicine ER -
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