Silva, Nelson ; Settgast, Volker ; Eggeling, Eva ; Grill, Florian ; Zeh, Theodor ; Fellner, Dieter W. (2014)
Sixth Sense - Air Traffic Control Prediction Scenario Augmented by Sensors.
i-KNOW 2014.
doi: 10.1145/2637748.2638441
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
This paper is focused on the fault tolerance of Human Machine Interfaces in the field of air traffic control (ATC) by accepting the overall user's body language as input. We describe ongoing work in progress in the project called Sixth Sense. Interaction patterns are reasoned from the combination of a recommendation and inference engine, the analysis of several graph database relationships and from multiple sensor raw data aggregations. Altogether, these techniques allow us to judge about different possible meanings of the current user's interaction and cognitive state. The results obtained from applying different machine learning techniques will be used to make recommendations and predictions on the user's actions. They are currently monitored and rated by a human supervisor.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2014 |
| Autor(en): | Silva, Nelson ; Settgast, Volker ; Eggeling, Eva ; Grill, Florian ; Zeh, Theodor ; Fellner, Dieter W. |
| Art des Eintrags: | Bibliographie |
| Titel: | Sixth Sense - Air Traffic Control Prediction Scenario Augmented by Sensors |
| Sprache: | Englisch |
| Publikationsjahr: | 2014 |
| Verlag: | ACM, New York |
| Reihe: | ACM International Conference Proceedings Series; 889 |
| Veranstaltungstitel: | i-KNOW 2014 |
| DOI: | 10.1145/2637748.2638441 |
| Kurzbeschreibung (Abstract): | This paper is focused on the fault tolerance of Human Machine Interfaces in the field of air traffic control (ATC) by accepting the overall user's body language as input. We describe ongoing work in progress in the project called Sixth Sense. Interaction patterns are reasoned from the combination of a recommendation and inference engine, the analysis of several graph database relationships and from multiple sensor raw data aggregations. Altogether, these techniques allow us to judge about different possible meanings of the current user's interaction and cognitive state. The results obtained from applying different machine learning techniques will be used to make recommendations and predictions on the user's actions. They are currently monitored and rated by a human supervisor. |
| Freie Schlagworte: | Business Field: Visual decision support, Research Area: Human computer interaction (HCI), Forschungsgruppe Semantic Models, Immersive Systems (SMIS), Inference, Mental maps, Sensor fusion, Machine learning, Expert systems, Asynchronous transfer mode (ATM), Human factors, Experiments, Verification |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Nov 2018 11:16 |
| Letzte Änderung: | 04 Feb 2022 12:39 |
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