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Ecological Momentary Assessment based Differences between Android and iOS Users of the TrackYourHearing mHealth Crowdsensing Platform.

, , , , , , , , , , , , , and . EMBC, page 3951-3955. IEEE, (2019)

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Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios, , , , , , , , , and 1 other author(s). JoVE (Journal of Visualized Experiments), (2020)Anomaly Detections for Manufacturing Systems Based on Sensor Data - Insights into Two Challenging Real-World Production Settings., , , , , and . Sensors, 19 (24): 5370 (2019)Datenverständnis in Predictive Maintenance Anwendungen im Zeitalter von Industrie 4.0. University of Ulm, Germany, (2023)base-search.net (ftunivulm:oai:oparu.uni-ulm.de:123456789/48572).Detecting Production Phases Based on Sensor Values using 1D-CNNs., , , , , and . CoRR, (2020)Towards a Hierarchical Approach for Outlier Detection in Industrial Production Settings., , , , and . EDBT/ICDT Workshops, volume 2322 of CEUR Workshop Proceedings, CEUR-WS.org, (2019)Convolutional Neural Networks for Image Recognition in Mixed Reality Using Voice Command Labeling., , , , and . AVR (2), volume 11614 of Lecture Notes in Computer Science, page 63-70. Springer, (2019)Ecological Momentary Assessment based Differences between Android and iOS Users of the TrackYourHearing mHealth Crowdsensing Platform., , , , , , , , , and 4 other author(s). EMBC, page 3951-3955. IEEE, (2019)Applying Machine Learning on the Daily Life Data of the TrackYourTinnitus mHealth Crowdsensing Platform Predicts the Mobile Operating System with High Accuracy, , , , , , and . (2019)Applicability of Immersive Analytics in Mixed Reality: Usability Study, , , , , , , , , and 1 other author(s). IEEE Access, (2019)Machine Learning Findings on Geospatial Data of Users from the TrackYourStress mHealth Crowdsensing Platform., , , , , , , , , and 2 other author(s). IRI, page 350-355. IEEE, (2019)