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Emotion Recognition Based on Sensors (3rd Edition)
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Emotion Recognition Based on Sensors (3rd Edition)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 20 January 2025 | Viewed by 1691

Special Issue Editor

Dr. Mariusz Szwoch

E-Mail Website
Guest Editor
Electronics, Telecommunications and Informatics Faculty, Gdansk University of Technology, Gdańsk, Poland
Interests: affective computing; emotion recognition; video games development; image processing; computer vision; machine learning; 3D graphics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Affective computing is an emerging field of computer science that plays, and will continue to play, an increasingly important role in human–computer interaction. The recognition of user emotions is a fundamental and viable element of each affective and affect-aware system. In recent years, many approaches to emotion recognition that use different input devices and channels, as well as different reasoning algorithms, have been proposed and developed. Various sensors, connected to or embedded in computer devices, smartphones, and training devices for fitness, health, and everyday use, play a special role in providing input data for such systems. They include, among others, cameras, microphones, depth sensors, biometric sensors, etc.

This Special Issue of the journal Sensors is focused on emotion recognition methods based on such sensory data. We are inviting original research articles covering novel theories, innovative machine learning methods, and meaningful applications that can potentially lead to significant advances in this field. The goal is to collect a diverse set of articles on emotion recognition that explore a wide range of sensors, data modalities, and their fusion and classification.

Dr. Mariusz Szwoch
Guest Editor

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. Sensors 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 2600 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

  • emotion recognition
  • emotion sensing
  • affective computing
  • sensory data processing
  • sensors
  • human–computer interaction
  • biomedical signal processing

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Related Special Issue

Published Papers (3 papers)

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Research

23 pages, 4573 KiB  
Article
AVaTER: Fusing Audio, Visual, and Textual Modalities Using Cross-Modal Attention for Emotion Recognition
by Avishek Das, Moumita Sen Sarma, Mohammed Moshiul Hoque, Nazmul Siddique and M. Ali Akber Dewan
Sensors 2024, 24(18), 5862; https://doi.org/10.3390/s24185862 - 10 Sep 2024
Viewed by 545
Abstract
Multimodal emotion classification (MEC) involves analyzing and identifying human emotions by integrating data from multiple sources, such as audio, video, and text. This approach leverages the complementary strengths of each modality to enhance the accuracy and robustness of emotion recognition systems. However, one [...] Read more.
Multimodal emotion classification (MEC) involves analyzing and identifying human emotions by integrating data from multiple sources, such as audio, video, and text. This approach leverages the complementary strengths of each modality to enhance the accuracy and robustness of emotion recognition systems. However, one significant challenge is effectively integrating these diverse data sources, each with unique characteristics and levels of noise. Additionally, the scarcity of large, annotated multimodal datasets in Bangla limits the training and evaluation of models. In this work, we unveiled a pioneering multimodal Bangla dataset, MAViT-Bangla (Multimodal Audio Video Text Bangla dataset). This dataset, comprising 1002 samples across audio, video, and text modalities, is a unique resource for emotion recognition studies in the Bangla language. It features emotional categories such as anger, fear, joy, and sadness, providing a comprehensive platform for research. Additionally, we developed a framework for audio, video and textual emotion recognition (i.e., AVaTER) that employs a cross-modal attention mechanism among unimodal features. This mechanism fosters the interaction and fusion of features from different modalities, enhancing the model’s ability to capture nuanced emotional cues. The effectiveness of this approach was demonstrated by achieving an F1-score of 0.64, a significant improvement over unimodal methods. Full article
(This article belongs to the Special Issue Emotion Recognition Based on Sensors (3rd Edition))
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39 pages, 6629 KiB  
Article
A Combined CNN Architecture for Speech Emotion Recognition
by Rolinson Begazo, Ana Aguilera, Irvin Dongo and Yudith Cardinale
Sensors 2024, 24(17), 5797; https://doi.org/10.3390/s24175797 - 6 Sep 2024
Viewed by 592
Abstract
Emotion recognition through speech is a technique employed in various scenarios of Human–Computer Interaction (HCI). Existing approaches have achieved significant results; however, limitations persist, with the quantity and diversity of data being more notable when deep learning techniques are used. The lack of [...] Read more.
Emotion recognition through speech is a technique employed in various scenarios of Human–Computer Interaction (HCI). Existing approaches have achieved significant results; however, limitations persist, with the quantity and diversity of data being more notable when deep learning techniques are used. The lack of a standard in feature selection leads to continuous development and experimentation. Choosing and designing the appropriate network architecture constitutes another challenge. This study addresses the challenge of recognizing emotions in the human voice using deep learning techniques, proposing a comprehensive approach, and developing preprocessing and feature selection stages while constructing a dataset called EmoDSc as a result of combining several available databases. The synergy between spectral features and spectrogram images is investigated. Independently, the weighted accuracy obtained using only spectral features was 89%, while using only spectrogram images, the weighted accuracy reached 90%. These results, although surpassing previous research, highlight the strengths and limitations when operating in isolation. Based on this exploration, a neural network architecture composed of a CNN1D, a CNN2D, and an MLP that fuses spectral features and spectogram images is proposed. The model, supported by the unified dataset EmoDSc, demonstrates a remarkable accuracy of 96%. Full article
(This article belongs to the Special Issue Emotion Recognition Based on Sensors (3rd Edition))
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17 pages, 6610 KiB  
Article
Fusion of PCA and ICA in Statistical Subset Analysis for Speech Emotion Recognition
by Rafael Kingeski, Elisa Henning and Aleksander S. Paterno
Sensors 2024, 24(17), 5704; https://doi.org/10.3390/s24175704 - 2 Sep 2024
Viewed by 357
Abstract
Speech emotion recognition is key to many fields, including human–computer interaction, healthcare, and intelligent assistance. While acoustic features extracted from human speech are essential for this task, not all of them contribute to emotion recognition effectively. Thus, reduced numbers of features are required [...] Read more.
Speech emotion recognition is key to many fields, including human–computer interaction, healthcare, and intelligent assistance. While acoustic features extracted from human speech are essential for this task, not all of them contribute to emotion recognition effectively. Thus, reduced numbers of features are required within successful emotion recognition models. This work aimed to investigate whether splitting the features into two subsets based on their distribution and then applying commonly used feature reduction methods would impact accuracy. Filter reduction was employed using the Kruskal–Wallis test, followed by principal component analysis (PCA) and independent component analysis (ICA). A set of features was investigated to determine whether the indiscriminate use of parametric feature reduction techniques affects the accuracy of emotion recognition. For this investigation, data from three databases—Berlin EmoDB, SAVEE, and RAVDES—were organized into subsets according to their distribution in applying both PCA and ICA. The results showed a reduction from 6373 features to 170 for the Berlin EmoDB database with an accuracy of 84.3%; a final size of 130 features for SAVEE, with a corresponding accuracy of 75.4%; and 150 features for RAVDESS, with an accuracy of 59.9%. Full article
(This article belongs to the Special Issue Emotion Recognition Based on Sensors (3rd Edition))
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