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VEO-Engine: Interfacing and Reasoning with an Emotion Ontology for Device Visual Expression

  • Muhammad Amith
  • Rebecca Lin
  • Chen Liang
  • Yang Gong
  • Cui Tao
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 851)

Abstract

In order for machines to understand or express emotion to users, the specific emotions must be formally defined and the software coded to how those emotions are to be expressed. This is particularly important if devices or computer-based tools are utilized in clinical settings, which may be stressful for patients and where emotions can dominate their decision making. We have reported our development and feasibility results of an ontology, Visualized Emotion Ontology (VEO), that links abstract visualizations that express specific emotions. Here, we used VEO with the VEO-Engine, a software API package that interfaces with the VEO. The VEO-Engine was developed in Java using Apache Jena and OWL-API. The software package was tested on a Raspberry Pi machine with a small touchscreen display that linked each visualization to an emotion. The VEO-Engine stores input parameters of emotional situations and valences to reason and interpret users’ emotions using the ontology-based reasoner. With this software, devices can interfaced wirelessly, so smart devices with visual displays can interact with the ontology. By means of the VEO-Engine, we show the portability and usability of the VEO in human-computer interaction.

Keywords

Ontology Emotions Semantic web mHealth Affective computing 

Notes

Acknowledgments

This research was supported by the UTHealth Innovation for Cancer Prevention Research Training Program (Cancer Prevention and Research Institute of Texas grant #RP160015), the National Library of Medicine of the National Institutes of Health award #R01LM011829, and the National Institute of Allergy and Infectious Diseases of the National Institutes of Health award #R01AI130460.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Muhammad Amith
    • 1
  • Rebecca Lin
    • 2
  • Chen Liang
    • 3
  • Yang Gong
    • 1
  • Cui Tao
    • 1
  1. 1.University of Texas Health Science CenterHoustonUSA
  2. 2.Johns Hopkins UniversityBaltimoreUSA
  3. 3.Lousiana Tech UniversityRustonUSA

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