ArticleBMC medical informatics and decision making2018
Visualized Emotion Ontology: a model for representing visual cues of emotions.
Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Knowledge Engineering Framework for IoT Robotics Applied to Smart Healthcare and Emotional Well-Being.International journal of social robotics · 2023Article
- Emotion Detection for Social Robots Based on NLP Transformers and an Emotion Ontology.Sensors (Basel, Switzerland) · 2021Article
- Examining Potential Usability and Health Beliefs Among Young Adults Using a Conversational Agent for HPV Vaccine Counseling.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Early Usability Assessment of a Conversational Agent for HPV Vaccination.Studies in health technology and informatics · 2019Article
- Introduction: selected extended articles from the 2nd International Workshop on Semantics-Powered Data Analytics (SEPDA 2017).BMC medical informatics and decision making · 2018Article
- VEO-Engine: Interfacing and Reasoning with an Emotion Ontology for Device Visual Expression.HCI International 2018 - posters' extended abstracts : 20th International Conference, HCI International 2018, Las Vegas, NV, USA, July 15-20, 2018, Proceedings, Part I. International Conference on Human-Computer Interaction (20th : 2018... · 2018Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundHealthcare services, particularly in patient-provider interaction, often involve highly emotional situations, and it is important for physicians to understand and respond to their patients' emotions to best ensure their well-being.
methodsIn order to model the emotion domain, we have created the Visualized Emotion Ontology (VEO) to provide a semantic definition of 25 emotions based on established models, as well as visual representations of emotions utilizing shapes, lines, and colors.
resultsAs determined by ontology evaluation metrics, VEO exhibited better machine-readability (z=1.12), linguistic quality (z=0.61), and domain coverage (z=0.39) compared to a sample of cognitive ontologies. Additionally, a survey of 1082 participants through Amazon Mechanical Turk revealed that a significantly higher proportion of people agree than disagree with 17 out of our 25 emotion images, validating the majority of our visualizations.
conclusionFrom the development, evaluation, and serialization of the VEO, we have defined a set of 25 emotions using OWL that linked surveyed visualizations to each emotion. In the future, we plan to use the VEO in patient-facing software tools, such as embodied conversational agents, to enhance interactions between patients and providers in a clinical environment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.