Dr. Robin Kraft
Kurzer Lebenslauf
2020-2021 und seit 2024 | Wissenschaftlicher Mitarbeiter am Institut für Klinische Epidemiologie und Biometrie, Universität Würzburg |
2017-2023 | Wissenschaftlicher Mitarbeiter am Institut für Datenbanken und Informationssysteme, Universität Ulm |
2015-2017 | Studium M.Sc. Medieninformatik, Schwerpunkte Informationssysteme & Mobile Application Engineering, Universität Ulm |
2011-2015 | Studium B.Sc. Medieninformatik, Universität Ulm |
Wissenschaftliche Schwerpunkte
- Mobile Crowdsensing (MCS)
- Ecological Momentary Assessment (EMA)
- eHealth & mHealth
- Smart Sensing
Ausgewählte Publikationen der letzten Jahre
2024[ to top ]
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Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and Analysis. Sensors (Basel). 2024;24(2)..
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2023[ to top ]
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Predicting the presence of tinnitus using ecological momentary assessments. Scientific Reports [Internet]. 2023;13(1):8989. Available from: https://doi.org/10.1038/s41598-023-36172-7.
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Exploring the usability of an internet-based intervention and its providing eHealth platform in an eye-tracking study. J Ambient Intell Humaniz Comput. 2023;14(7):9621-36..
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2022[ to top ]
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Dealing With Inaccurate Sensor Data in the Context of Mobile Crowdsensing and mHealth. IEEE J Biomed Health Inform. 2022;26(11):5439-4..
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Backend Concept of the eSano eHealth Platform for Internet- and Mobile-based Interventions. In: 2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). 2022. pp. 88-93..
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Predicting Ecological Momentary Assessments in an App for Tinnitus by Learning From Each User’s Stream With a Contextual Multi-Armed Bandit. Frontiers in Neuroscience. 2022;16:836834..
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Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices. Sensors [Internet]. 2022;22(1):170. Available from: https://www.mdpi.com/1424-8220/22/1/170.
2021[ to top ]
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User-centric vs whole-stream learning for EMA prediction. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS). 2021. pp. 307-12..
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eSano – An eHealth Platform for Internet- and Mobile-based Interventions. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2021. pp. 1997-2002..
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Literature-based requirements analysis review of persuasive systems design for mental health applications. Procedia Computer Science [Internet]. 2021;191:143-50. Available from: https://www.sciencedirect.com/science/article/pii/S1877050921014137.
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Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users. Entropy [Internet]. 2021;23(12):1695. Available from: https://www.mdpi.com/1099-4300/23/12/1695.
2020[ to top ]
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Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform. Sensors [Internet]. 2020;20(12):3456. Available from: https://www.mdpi.com/1424-8220/20/12/3456.
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The Effect of Non-Personalised Tips on the Continued Use of Self-Monitoring mHealth Applications. Brain Sciences [Internet]. 2020;10(12):924. Available from: https://www.mdpi.com/2076-3425/10/12/924.
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Mobile Health App Database - A Repository for Quality Ratings of mHealth Apps. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). 2020. pp. 427-32..
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Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain. Frontiers in Neuroscience [Internet]. 2020;14. Available from: https://www.frontiersin.org/articles/10.3389/fnins.2020.00164.
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