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Sixth Sense - Air Traffic Control Prediction Scenario Augmented by Sensors

Silva, Nelson and Settgast, Volker and Eggeling, Eva and Grill, Florian and Zeh, Theodor and Fellner, Dieter W. (2014):
Sixth Sense - Air Traffic Control Prediction Scenario Augmented by Sensors.
ACM, New York, In: i-KNOW 2014, In: ACM International Conference Proceedings Series; 889, DOI: 10.1145/2637748.2638441,
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2014
Creators: Silva, Nelson and Settgast, Volker and Eggeling, Eva and Grill, Florian and Zeh, Theodor and Fellner, Dieter W.
Title: Sixth Sense - Air Traffic Control Prediction Scenario Augmented by Sensors
Language: English
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.

Series Name: ACM International Conference Proceedings Series; 889
Publisher: ACM, New York
Uncontrolled Keywords: 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
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: i-KNOW 2014
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1145/2637748.2638441
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