@article{SchicklerReichertGeigeretal.2020,
  author    = {Schickler, Marc and Reichert, Manfred and Geiger, Philip and Winkler, Jens and Funk, Thomas and Weilbach, Micha and Pryss, R{\"u}diger},
  title     = {Flexible development of location-based mobile augmented reality applications with AREA},
  series = {Journal of Ambient Intelligence and Humanized Computing},
  volume    = {11},
  journal   = {Journal of Ambient Intelligence and Humanized Computing},
  issn      = {1868-5137},
  doi       = {10.1007/s12652-020-02094-9},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-232773},
  pages     = {5809-5824},
  year      = {2020},
  abstract  = {Mobile applications have garnered a lot of attention in the last years. The computational capabilities of mobile devices are the mainstay to develop completely new application types. The provision of augmented reality experiences on mobile devices paves one alley in this field. For example, in the automotive domain, augmented reality applications are used to experience, inter alia, the interior of a car by moving a mobile device around. The device's camera then detects interior parts and shows additional information to the customer within the camera view. Another application type that is increasingly utilized is related to the combination of serious games with mobile augmented reality functions. Although the latter combination is promising for many scenarios, technically, it is a complex endeavor. In the AREA (Augmented Reality Engine Application) project, a kernel was implemented that enables location-based mobile augmented reality applications. Importantly, this kernel provides a flexible architecture that fosters the development of individual location-based mobile augmented reality applications. The work at hand shows the flexibility of AREA based on a developed serious game. Furthermore, the algorithm framework and major features of it are presented. As the conclusion of this paper, it is shown that mobile augmented reality applications require high development efforts. Therefore, flexible frameworks like AREA are crucial to develop respective applications in a reasonable time.},
  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}
}
@article{KraftSchleeStachetal.2020,
  author    = {Kraft, Robin and Schlee, Winfried and Stach, Michael and Reichert, Manfred and Langguth, Berthold and Baumeister, Harald and Probst, Thomas and Hannemann, Ronny and Pryss, R{\"u}diger},
  title     = {Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain},
  series = {Frontiers in Neuroscience},
  volume    = {14},
  journal   = {Frontiers in Neuroscience},
  number    = {164},
  issn      = {1662-453X},
  doi       = {10.3389/fnins.2020.00164},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-200220},
  year      = {2020},
  abstract  = {The increasing prevalence of smart mobile devices (e.g., smartphones) enables the combined use of mobile crowdsensing (MCS) and ecological momentary assessments (EMA) in the healthcare domain. By correlating qualitative longitudinal and ecologically valid EMA assessment data sets with sensor measurements in mobile apps, new valuable insights about patients (e.g., humans who suffer from chronic diseases) can be gained. However, there are numerous conceptual, architectural and technical, as well as legal challenges when implementing a respective software solution. Therefore, the work at hand (1) identifies these challenges, (2) derives respective recommendations, and (3) proposes a reference architecture for a MCS-EMA-platform addressing the defined recommendations. The required insights to propose the reference architecture were gained in several large-scale mHealth crowdsensing studies running for many years and different healthcare questions. To mention only two examples, we are running crowdsensing studies on questions for the tinnitus chronic disorder or psychological stress. We consider the proposed reference architecture and the identified challenges and recommendations as a contribution in two respects. First, they enable other researchers to align our practical studies with a baseline setting that can satisfy the variously revealed insights. Second, they are a proper basis to better compare data that was gathered using MCS and EMA. In addition, the combined use of MCS and EMA increasingly requires suitable architectures and associated digital solutions for the healthcare domain.},
  language  = {en}
}
@article{KammererHoppenstedtPryssetal.2019,
  author    = {Kammerer, Klaus and Hoppenstedt, Burkhard and Pryss, R{\"u}diger and St{\"o}kler, Steffen and Allgaier, Johannes and Reichert, Manfred},
  title     = {Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings},
  series = {Sensors},
  volume    = {19},
  journal   = {Sensors},
  number    = {24},
  issn      = {1424-8220},
  doi       = {10.3390/s19245370},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193885},
  pages     = {5370},
  year      = {2019},
  abstract  = {o build, run, and maintain reliable manufacturing machines, the condition of their components has to be continuously monitored. When following a fine-grained monitoring of these machines, challenges emerge pertaining to the (1) feeding procedure of large amounts of sensor data to downstream processing components and the (2) meaningful analysis of the produced data. Regarding the latter aspect, manifold purposes are addressed by practitioners and researchers. Two analyses of real-world datasets that were generated in production settings are discussed in this paper. More specifically, the analyses had the goals (1) to detect sensor data anomalies for further analyses of a pharma packaging scenario and (2) to predict unfavorable temperature values of a 3D printing machine environment. Based on the results of the analyses, it will be shown that a proper management of machines and their components in industrial manufacturing environments can be efficiently supported by the detection of anomalies. The latter shall help to support the technical evangelists of the production companies more properly.},
  language  = {en}
}