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Application of Affective Computing
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Application of Affective Computing

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 February 2025 | Viewed by 8079

Special Issue Editors

Dr. Xin Liu

E-Mail Website
Guest Editor
Department of Computational Engineering, School of Engineering Sciences, Lappeenranta-Lahti University of Technology LUT, 53850 Lappeenranta, Finland
Interests: affective computing; micro-gesture; emotion AI; social signal processing; rPPG
Dr. Jingang Shi

E-Mail Website
Guest Editor
School of Software, Xi’an Jiaotong University, Xi’an 710049, China
Interests: face analysis; emotion analysis; image restoration; physiological signal analysis; action recognition; deep learning
Dr. Yuan Zong

E-Mail Website
Guest Editor
School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
Interests: facial expression analysis; micro-expression analysis; speech emotion recognition; multi-modal emotion recognition; EEG emotion recognition; domain adaptation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to invite you to submit your research work to our Special Issue, “Application of Affective Computing”.

This Special Issue aims to provide a platform for researchers to share their novel contributions related to machine learning and deep learning methods for affective computing and the application of affective computing. Affective computing is a field that focuses on enabling machines to automatically perceive, recognize, and express emotions from/with multimodal signals, such as video, image, audio, and text. If machines could understand emotions in a similar way to humans, existing human–computer interaction systems would become more natural. With the advancements in deep learning models and the use of well-designed structures and loss functions, affective computing has achieved significant progress and presented promising prospect in recent years. This Special Issue encourages contributions related to unimodal and multimodal affective computing, emotional signal synthesis and conversion, large-scale databases, recent advances in affective computing, as well as applications of affective computing in various fields such as healthcare, education, entertainment, and security.

Broad topics/keywords and areas of interest include but are not limited to:

  • Secure use of data in affective computing;
  • Identity-free affective computing;
  • Unimodal and multimodal affective computing;
  • Emotional signal synthesis and conversion;
  • Large-scale databases on affective computing;
  • Recent advances in affective computing;
  • Applications of affective computing in various fields such as healthcare, education, and entertainment.

Kind Regards,

Dr. Xin Liu
Dr. Jingang Shi
Dr. Yuan Zong
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
  • multimodal information fusion
  • emotion database
  • emotional signal generation and conversion
  • deep neural networks

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Published Papers (5 papers)

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Research

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17 pages, 4630 KiB  
Article
Evaluating Airport Service Quality Based on the Statistical and Predictive Analysis of Skytrax Passenger Reviews
by Mohammed Saad M. Alanazi, Jun Li and Karl W. Jenkins
Appl. Sci. 2024, 14(20), 9472; https://doi.org/10.3390/app14209472 - 17 Oct 2024
Viewed by 554
Abstract
This study leverages approximately 7500 reviews from Skytrax to explore the determinants of airport service quality and their influence on passenger recommendations. The dataset includes various features such as terminal cleanliness, terminal seating, terminal signs, food and beverages, airport shopping, WiFi connectivity, and [...] Read more.
This study leverages approximately 7500 reviews from Skytrax to explore the determinants of airport service quality and their influence on passenger recommendations. The dataset includes various features such as terminal cleanliness, terminal seating, terminal signs, food and beverages, airport shopping, WiFi connectivity, and airport staff. The research employs a comprehensive methodology encompassing statistical data analysis, predictive modelling, and interaction effects analysis. The descriptive analysis of time-series data highlighted trends and fluctuations in service quality and recommendations, providing insights into temporal dynamics. Multiple machine learning models, including logistic regression, Random Forest, SVM, KNN, Gradient Boosting, and Neural Networks, were developed in this study and cross-validated for airport recommendation based on Skytrax’s online reviews. Among others, Gradient Boosting emerged as the most accurate model with an 88.15% mean accuracy. Interaction effects revealed significant combined influences, such as terminal cleanliness and terminal seating, on passenger recommendations. This multifaceted approach offers robust insights into factors influencing airport recommendations and guides improvements in airport management to enhance passenger satisfaction. Future work will focus on a general-purpose machine learning framework and its toolbox development for airport service quality analysis based on online reviews from various sources. Full article
(This article belongs to the Special Issue Application of Affective Computing)
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11 pages, 607 KiB  
Article
A Semi-Supervised Lie Detection Algorithm Based on Integrating Multiple Speech Emotional Features
by Ji Xi, Hang Yu, Zhe Xu, Li Zhao and Huawei Tao
Appl. Sci. 2024, 14(16), 7391; https://doi.org/10.3390/app14167391 - 21 Aug 2024
Viewed by 693
Abstract
When people tell lies, they often exhibit tension and emotional fluctuations, reflecting a complex psychological state. However, the scarcity of labeled data in datasets and the complexity of deception information pose significant challenges in extracting effective lie features, which severely restrict the accuracy [...] Read more.
When people tell lies, they often exhibit tension and emotional fluctuations, reflecting a complex psychological state. However, the scarcity of labeled data in datasets and the complexity of deception information pose significant challenges in extracting effective lie features, which severely restrict the accuracy of lie detection systems. To address this, this paper proposes a semi-supervised lie detection algorithm based on integrating multiple speech emotional features. Firstly, Long Short-Term Memory (LSTM) and Auto Encoder (AE) network process log Mel spectrogram features and acoustic statistical features, respectively, to capture the contextual links between similar features. Secondly, the joint attention model is used to learn the complementary relationship among different features to obtain feature representations with richer details. Lastly, the model combines the unsupervised loss Local Maximum Mean Discrepancy (LMMD) and supervised loss Jefferys multi-loss optimization to enhance the classification performance. Experimental results show that the algorithm proposed in this paper achieves better performance. Full article
(This article belongs to the Special Issue Application of Affective Computing)
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13 pages, 2696 KiB  
Article
High-Accuracy Classification of Multiple Distinct Human Emotions Using EEG Differential Entropy Features and ResNet18
by Longxin Yao, Yun Lu, Yukun Qian, Changjun He and Mingjiang Wang
Appl. Sci. 2024, 14(14), 6175; https://doi.org/10.3390/app14146175 - 16 Jul 2024
Viewed by 668
Abstract
The high-accuracy detection of multiple distinct human emotions is crucial for advancing affective computing, mental health diagnostics, and human–computer interaction. The integration of deep learning networks with entropy measures holds significant potential in neuroscience and medicine, especially for analyzing EEG-based emotion states. This [...] Read more.
The high-accuracy detection of multiple distinct human emotions is crucial for advancing affective computing, mental health diagnostics, and human–computer interaction. The integration of deep learning networks with entropy measures holds significant potential in neuroscience and medicine, especially for analyzing EEG-based emotion states. This study proposes a method combining ResNet18 with differential entropy to identify five types of human emotions (happiness, sadness, fear, disgust, and neutral) from EEG signals. Our approach first calculates the differential entropy of EEG signals to capture the complexity and variability of the emotional states. Then, the ResNet18 network is employed to learn feature representations from the differential entropy measures, which effectively captures the intricate spatiotemporal dynamics inherent in emotional EEG patterns using residual connections. To validate the efficacy of our method, we conducted experiments on the SEED-V dataset, achieving an average accuracy of 95.61%. Our findings demonstrate that the combination of ResNet18 with differential entropy is highly effective in classifying multiple distinct human emotions from EEG signals. This method shows robust generalization and broad applicability, indicating its potential for extension to various pattern recognition tasks across different domains. Full article
(This article belongs to the Special Issue Application of Affective Computing)
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14 pages, 5092 KiB  
Article
Real-Time Analysis of Facial Expressions for Mood Estimation
by Juan Sebastián Filippini, Javier Varona and Cristina Manresa-Yee
Appl. Sci. 2024, 14(14), 6173; https://doi.org/10.3390/app14146173 - 16 Jul 2024
Viewed by 581
Abstract
This paper proposes a model-based method for real-time automatic mood estimation in video sequences. The approach is customized by learning the person’s specific facial parameters, which are transformed into facial Action Units (AUs). A model mapping for mood representation is used to describe [...] Read more.
This paper proposes a model-based method for real-time automatic mood estimation in video sequences. The approach is customized by learning the person’s specific facial parameters, which are transformed into facial Action Units (AUs). A model mapping for mood representation is used to describe moods in terms of the PAD space: Pleasure, Arousal, and Dominance. From the intersection of these dimensions, eight octants represent fundamental mood categories. In the experimental evaluation, a stimulus video randomly selected from a set prepared to elicit different moods was played to participants, while the participant’s facial expressions were recorded. From the experiment, Dominance is the dimension least impacted by facial expression, and this dimension could be eliminated from mood categorization. Then, four categories corresponding to the quadrants of the Pleasure–Arousal (PA) plane, “Exalted”, “Calm”, “Anxious” and “Bored”, were defined, with two more categories for the “Positive” and “Negative” signs of the Pleasure (P) dimension. Results showed a 73% of coincidence in the PA categorization and a 94% in the P dimension, demonstrating that facial expressions can be used to estimate moods, within these defined categories, and provide cues for assessing users’ subjective states in real-world applications. Full article
(This article belongs to the Special Issue Application of Affective Computing)
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Other

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25 pages, 2680 KiB  
Systematic Review
A Systematic Literature Review of Modalities, Trends, and Limitations in Emotion Recognition, Affective Computing, and Sentiment Analysis
by Rosa A. García-Hernández, Huizilopoztli Luna-García, José M. Celaya-Padilla, Alejandra García-Hernández, Luis C. Reveles-Gómez, Luis Alberto Flores-Chaires, J. Ruben Delgado-Contreras, David Rondon and Klinge O. Villalba-Condori
Appl. Sci. 2024, 14(16), 7165; https://doi.org/10.3390/app14167165 - 15 Aug 2024
Viewed by 4566
Abstract
This systematic literature review delves into the extensive landscape of emotion recognition, sentiment analysis, and affective computing, analyzing 609 articles. Exploring the intricate relationships among these research domains, and leveraging data from four well-established sources—IEEE, Science Direct, Springer, and MDPI—this systematic review classifies [...] Read more.
This systematic literature review delves into the extensive landscape of emotion recognition, sentiment analysis, and affective computing, analyzing 609 articles. Exploring the intricate relationships among these research domains, and leveraging data from four well-established sources—IEEE, Science Direct, Springer, and MDPI—this systematic review classifies studies in four modalities based on the types of data analyzed. These modalities are unimodal, multi-physical, multi-physiological, and multi-physical–physiological. After the classification, key insights about applications, learning models, and data sources are extracted and analyzed. This review highlights the exponential growth in studies utilizing EEG signals for emotion recognition, and the potential of multimodal approaches combining physical and physiological signals to enhance the accuracy and practicality of emotion recognition systems. This comprehensive overview of research advances, emerging trends, and limitations from 2018 to 2023 underscores the importance of continued exploration and interdisciplinary collaboration in these rapidly evolving fields. Full article
(This article belongs to the Special Issue Application of Affective Computing)
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