Conceptual Human Emotion Modeling (HEM)

Elkobaisi, Mohammed R.; Mayr, Heinrich C.; Shekhovtsov, Vladimir A.

doi:10.1007/978-3-030-65847-2_7

Cited by 4 publications

(1 citation statement)

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“…The current approach provides more benefits such as specialized syntax and error checking in the modeling environment. The functionality of our approach can be reused as a plug-in (Elkobaisi et al 2020) among multiple applications that leads to increase the productivity.…”

Section: Discussionmentioning

confidence: 99%

Human emotion modeling (HEM): an interface for IoT systems

Elkobaisi

Machot

2021

J Ambient Intell Human Comput

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The use of IoT-based Emotion Recognition (ER) systems is in increasing demand in many domains such as active and assisted living (AAL), health care and industry. Combining the emotion and the context in a unified system could enhance the human support scope, but it is currently a challenging task due to the lack of a common interface that is capable to provide such a combination. In this sense, we aim at providing a novel approach based on a modeling language that can be used even by care-givers or non-experts to model human emotion w.r.t. context for human support services. The proposed modeling approach is based on Domain-Specific Modeling Language (DSML) which helps to integrate different IoT data sources in AAL environment. Consequently, it provides a conceptual support level related to the current emotional states of the observed subject. For the evaluation, we show the evaluation of the well-validated System Usability Score (SUS) to prove that the proposed modeling language achieves high performance in terms of usability and learn-ability metrics. Furthermore, we evaluate the performance at runtime of the model instantiation by measuring the execution time using well-known IoT services.

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Section: Discussionmentioning

confidence: 99%

Human emotion modeling (HEM): an interface for IoT systems

Elkobaisi

Machot

2021

J Ambient Intell Human Comput

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An Interactive Metamodel Integration Approach (IMIA) for Active and Assisted Living Systems

Elkobaisi,

Al Machot

2024

Studies in Computational Intelligence

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Human Emotion: A Survey focusing on Languages, Ontologies, Datasets, and Systems

2022

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Emotions are an essential part of a person’s mental state and influence her/his behavior accordingly. Consequently, emotion recognition and assessment can play an important role in supporting people with ambient assistance systems or clinical treatments. Automation of human emotion recognition and emotion-aware recommender systems are therefore increasingly being researched. In this paper, we first consider the essential aspects of human emotional functioning from the perspective of cognitive psychology and, based on this, we analyze the state of the art in the whole field of work and research to which automated emotion recognition belongs. In this way, we want to complement the already published surveys, which usually refer to only one aspect, with an overall overview of the languages ontologies, datasets, and systems/interfaces to be found in this area. We briefly introduce each of these subsections and discuss related approaches regarding methodology, technology, and publicly accessible artefacts. This comes with an update to recent findings that could not yet be taken into account in previous surveys. The paper is based on an extensive literature search and analysis, in which we also made a particular effort to locate relevant surveys and reviews. The paper closes with a summary of the results and an outlook on open research questions.

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Conceptual Human Emotion Modeling (HEM)

Cited by 4 publications

References 20 publications

Human emotion modeling (HEM): an interface for IoT systems

Human emotion modeling (HEM): an interface for IoT systems

An Interactive Metamodel Integration Approach (IMIA) for Active and Assisted Living Systems

Human Emotion: A Survey focusing on Languages, Ontologies, Datasets, and Systems

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