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Affective Computing: Technology and Application
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Affective Computing: Technology and Application

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 5914

Special Issue Editors

Dr. Andrea Generosi

E-Mail Website
Guest Editor
Department of Industrial Engineering and Mathematical Sciences, Polytechnic University of Marche, 60131 Ancona, AN, Italy
Interests: affective computing; machine learning; deep learning; human–machine interaction; adaptive interfaces; emotion- and context-aware interfaces; virtual and augmented reality; extended reality applications for cultural heritage
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maura Mengoni

E-Mail Website
Guest Editor
Department of Industrial Engineering and Mathematical Science (DIISM), Università Politecnica delle Marche, Piazza Roma, 22, 60121 Ancona, Italy
Interests: design theory; extended reality technologies; user experience; affective computing; human–machine interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of affective computing technologies is revolutionizing the interaction between humans and machines, driving advancements across various domains. This Special Issue aims to present cutting-edge research and experimental results in the field of affective computing, highlighting its technological advancements and practical implementations.

Areas relevant to affective computing include, but are not limited to, emotion recognition algorithms, affective user interfaces, sentiment analysis, affective robotics, and the impact of emotional data on decision-making processes. Research on multimodal emotion detection, the integration of affective computing in virtual and augmented reality, and the application of these technologies in healthcare, education, entertainment, and customer service is highly encouraged. Additionally, the ethical considerations and societal implications of affective computing will be addressed.

This Special Issue will publish high-quality, original research papers in the following overlapping fields:

  • Affective computing technologies;
  • Emotion recognition and sentiment analysis;
  • Affective user interfaces and interaction;
  • Multimodal emotion detection;
  • Affective robotics;
  • Applications of affective computing in healthcare, education, entertainment, and customer service;
  • Ethical considerations and societal impact of affective computing.

Dr. Andrea Generosi
Prof. Dr. Maura Mengoni
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • affective computing
  • emotion recognition
  • sentiment analysis
  • affective user interfaces
  • multimodal emotion detection
  • affective robotics
  • human–computer interaction
  • emotional data
  • affective extended reality
  • ethical considerations in affective computing

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Published Papers (2 papers)

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Research

21 pages, 5031 KiB  
Article
Evaluating the Role of Interactive Encouragement Prompts for Parents in Parent–Child Stress Management
by Pinhao Wang, Lening Huang, Guang Dai, Jing Li, Jun Hu, Emilia Barakova, Cheng Yao and Fangtian Ying
Appl. Sci. 2025, 15(1), 256; https://doi.org/10.3390/app15010256 - 30 Dec 2024
Viewed by 990
Abstract
Parental involvement is crucial for children’s stress management, and co-regulation of stress can have a positive effect. To facilitate parental involvement in children’s stress management in learning, we proposed an embodied connected system, which provides stress detection, stress information feedback, and encouragement prompts, [...] Read more.
Parental involvement is crucial for children’s stress management, and co-regulation of stress can have a positive effect. To facilitate parental involvement in children’s stress management in learning, we proposed an embodied connected system, which provides stress detection, stress information feedback, and encouragement prompts, aiming to help parents better understand and engage in children’s stress-regulation process. This article focuses on the impact of interactive encouragement prompts provided to parents on children’s stress management. The within-group experiment was used to collect stress data and scales from 36 parent–child groups during a controlled learning experiment, and semi-structured interviews were conducted with parents and children. The results indicate that the encouragement prompts provided to the parents enhance the effectiveness of stress relief in children facilitated by parental involvement. In particular, the psychological stress was reduced, and the communication between parents and children became more effective. In addition, active parental involvement and timely encouragement prompts can improve children’s stress-coping abilities, providing an interactive intervention approach for learning stress management. Full article
(This article belongs to the Special Issue Affective Computing: Technology and Application)
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21 pages, 6992 KiB  
Article
Performance Metrics for Multilabel Emotion Classification: Comparing Micro, Macro, and Weighted F1-Scores
by Maria Cristina Hinojosa Lee, Johan Braet and Johan Springael
Appl. Sci. 2024, 14(21), 9863; https://doi.org/10.3390/app14219863 - 28 Oct 2024
Cited by 8 | Viewed by 4217
Abstract
This study compares various F1-score variants—micro, macro, and weighted—to assess their performance in evaluating text-based emotion classification. Lexicon distillation is employed using the multilabel emotion-annotated datasets XED and GoEmotions. The aim of this paper is to understand when each F1-score variant is better [...] Read more.
This study compares various F1-score variants—micro, macro, and weighted—to assess their performance in evaluating text-based emotion classification. Lexicon distillation is employed using the multilabel emotion-annotated datasets XED and GoEmotions. The aim of this paper is to understand when each F1-score variant is better suited for evaluating text-based multilabel emotion classification. Unigram lexicons were derived from the annotated GoEmotions and XED datasets through a binary classification approach. The distilled lexicons were then applied to the GoEmotions and XED annotated datasets to calculate their emotional content, and the results were compared. The findings highlight the behavior of each F1-score variant under different class distributions, emphasizing the importance of appropriate metric selection for reliable model performance evaluation in imbalanced multilabel datasets. Additionally, this study also investigates the effect of the aggregation of negative emotions into broader categories on said F1 metrics. The contribution of this study is to provide insights into how different F1-score variants could improve the reliability of multilabel emotion classifier evaluation, particularly in the context of class imbalance present in the case of phishing emails. Full article
(This article belongs to the Special Issue Affective Computing: Technology and Application)
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