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

• Published in: Journal of ambient intelligence and humanized computing Vol. 13; no. 8; pp. 4009 - 4017
• Main Authors: Elkobaisi, Mohammed R., Al Machot, Fadi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2022; Springer Nature B.V
• Subjects: Artificial Intelligence; Automation; Cognition & reasoning; Computational Intelligence; Context; Emotion recognition; Emotional factors; Emotions; Engineering; Language; Modelling; Ontology; Original Research; Performance evaluation; Robotics and Automation; Semantics; Support services; Syntax; User Interfaces and Human Computer Interaction; Web Ontology Language-OWL; Domain-Specific Modeling Language (DSML); Active and assisted living (AAL); Human emotion (HE); Internet of things (IoT)
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-021-03437-w

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.