@article{KraftBirkReichertetal.2020,
  author    = {Kraft, Robin and Birk, Ferdinand and Reichert, Manfred and Deshpande, Aniruddha and Schlee, Winfried and Langguth, Berthold and Baumeister, Harald and Probst, Thomas and Spiliopoulou, Myra and Pryss, R{\"u}diger},
  title     = {Efficient processing of geospatial mHealth data using a scalable crowdsensing platform},
  series = {Sensors},
  volume    = {20},
  journal   = {Sensors},
  number    = {12},
  issn      = {1424-8220},
  doi       = {10.3390/s20123456},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207826},
  year      = {2020},
  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.},
  language  = {en}
}
@article{PryssSchleeHoppenstedtetal.2020,
  author    = {Pryss, R{\"u}diger and Schlee, Winfried and Hoppenstedt, Burkhard and Reichert, Manfred and Spiliopoulou, Myra and Langguth, Berthold and Breitmayer, Marius and Probst, Thomas},
  title     = {Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study},
  series = {Journal of Medical Internet Research},
  volume    = {22},
  journal   = {Journal of Medical Internet Research},
  number    = {6},
  doi       = {10.2196/15547},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229517},
  year      = {2020},
  abstract  = {Background: Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1\% to 42.7\% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient's quality of life. The TrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)-Android and iOS-to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider.},
  language  = {en}
}