Acknowledge of Emotions for Improving Student-Robot Interaction

Han, Hasan; Karadeniz, Oguzcan; Dalyan, Tuğba; Sönmez, Elena Battini; Sarioğlu, Baykal

doi:10.32604/iasc.2023.030674

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…Other initiatives combine the imitative aspect with identification, whereby emotions in individuals are identified and the machine imitates others in response to enhance interaction ( Gaeta et al, 2023 ; Han et al, 2023 ). The interaction of these imitative models with students can be carried out in various ways, such as through empathetic agents that can even be integrated into virtual reality systems ( Hernández and Ramírez, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Analysis of social metrics on scientific production in the field of emotion-aware education through artificial intelligence

Roda-Segarra,

Mengual-Andrés,

Payà Rico

2024

Front. Artif. Intell.

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Research in the field of Artificial Intelligence applied to emotions in the educational context has experienced significant growth in recent years. However, despite the field’s profound implications for the educational community, the social impact of this scientific production on digital social media remains unclear. To address this question, the present research has been proposed, aiming to analyze the social impact of scientific production on the use of Artificial Intelligence for emotions in the educational context. For this purpose, a sample of 243 scientific publications indexed in Scopus and Web of Science has been selected, from which a second sample of 6,094 social impact records has been extracted from Altmetric, Crossref, and PlumX databases. A dual analysis has been conducted using specially designed software: on one hand, the scientific sample has been analyzed from a bibliometric perspective, and on the other hand, the social impact records have been studied. Comparative analysis based on the two dimensions, scientific and social, has focused on the evolution of scientific production with its corresponding social impact, sources, impact, and content analysis. The results indicate that scientific publications have had a high social impact (with an average of 25.08 social impact records per publication), with a significant increase in research interest starting from 2019, likely driven by the emotional implications of measures taken to curb the COVID-19 pandemic. Furthermore, a lack of alignment has been identified between articles with the highest scientific impact and those with the highest social impact, as well as a lack of alignment in the most commonly used terms from both scientific and social perspectives, a significant variability in the lag in months for scientific research to make an impact on social media, and the fact that the social impact of the research did not emerge from the interest of Twitter users unaffiliated with the research, but rather from the authors, publishers, or scientific institutions. The proposed comparative methodology can be applied to any field of study, making it a useful tool given that current trends in accreditation agencies propose the analysis of the repercussion of scientific research in social media.

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Section: Introductionmentioning

confidence: 99%

Analysis of social metrics on scientific production in the field of emotion-aware education through artificial intelligence

Roda-Segarra,

Mengual-Andrés,

Payà Rico

2024

Front. Artif. Intell.

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An Emotion Recognition Method for Humanoid Robot Body Movements Based on a PSO-BP-RMSProp Neural Network

Gao,

Jiang,

Zhai

et al. 2024

Sensors

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This paper mainly explores the computational model that connects a robot’s emotional body movements with human emotion to propose an emotion recognition method for humanoid robot body movements. There is sparse research directly carried out from this perspective to recognize robot bodily expression. A robot’s body movements are designed by imitating human emotional body movements. Subjective questionnaires and statistical methods are used to analyze the characteristics of a user’s perceptions and select appropriate designs. An emotional body movement recognition model using a BP neural network (EBMR-BP model) is proposed, in which the selected robot’s body movements and corresponding emotions are used as inputs and outputs. The corresponding topological architecture, encoding rules, and training process are illustrated in detail. Then, the PSO method and the RMSProp algorithm are introduced to optimize the EBMR-BP method, and the PSO-BP-RMSProp model is developed. Through experiments and comparisons for emotion recognition of a robot’s body movements, the feasibility and effectiveness of the EBMR-BP model, with a recognition rate of 66.67%, and the PSO-BP-RMSProp model, with a recognition rate of 88.89%, are verified. This indicates that the proposed method can be used for emotion recognition of a robot’s body movements, and optimization can improve emotion recognition. The contributions are beneficial for emotional interaction design in HRI.

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Acknowledge of Emotions for Improving Student-Robot Interaction

Cited by 2 publications

References 16 publications

Analysis of social metrics on scientific production in the field of emotion-aware education through artificial intelligence

Analysis of social metrics on scientific production in the field of emotion-aware education through artificial intelligence

An Emotion Recognition Method for Humanoid Robot Body Movements Based on a PSO-BP-RMSProp Neural Network

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