Perceptive-Cognitive User Interface for Visual Analytics Systems

Abstract

The paper is devoted to using Internet of Things technologies for hardware human-machine interfaces development. Thanks to these technologies, it may be possible to improve the capabilities of visual analytics systems with multiple modalities: movements, audio, etc. It can speed up semantic data filtering and interpretation, increasing the efficiency of analytics. We suggest using ontology engineering methods and tools to automate both the programming of custom hardware human-machine interfaces and connecting them to the third-party software. The proposed concept is tested by solving the real-world tasks of discovering the relationships between the psychological characteristics of the native speakers and their verbal behavior.