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Advanced Signal Processing for Affective Computing
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Advanced Signal Processing for Affective Computing

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

Deadline for manuscript submissions: 25 August 2025 | Viewed by 1711

Special Issue Editors

Dr. Hugo F. Posada-Quintero

E-Mail Website
Guest Editor
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: electro-physiological signals; electrodermal activity; heart rate variability; electromyography; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Raul Fernandez Rojas

E-Mail Website
Guest Editor
School of Information Technology and Systems, University of Canberra, Canberra, ACT 2617, Australia
Interests: neurophysiological sensors; EEG; fNIRS; ECG; signal processing; cognitive computing
Special Issues, Collections and Topics in MDPI journals
Dr. Yedukondala Rao Veeranki

E-Mail Website
Guest Editor
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: nonlinear signal processing; electrodermal activity; electromyography; electroencephalogram; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Youngsun Kong

E-Mail Website
Guest Editor
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: wearable devices such as accelerometers, electrocardiograms, photoplethysmograms, and electrodermal activity sensors to monitor and understand human physiology in uncomfortable or challenging environments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Affective computing, the field dedicated to understanding and responding to human emotions, has seen significant advances driven by breakthroughs in biomedical sensing, especially in methods for processing such sensor-derived signals. This Special Issue will gather cutting-edge research that explores innovative signal processing techniques to accurately detect, recognize, and interpret human emotions, intentions, and physiological states. We request contributions that address the challenges of the acquisition, preprocessing, feature extraction, and classification of various physiological signals, such as EEG, ECG, EMG, EDA, and multimodal data. We encourage papers to demonstrate the practical application of the proposed methods in real-world scenarios, including but not limited to healthcare, human–computer interaction, and mental health.

Dr. Hugo F. Posada-Quintero
Dr. Raul Fernandez Rojas
Dr. Yedukondala Rao Veeranki
Dr. Youngsun Kong
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. 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

  • affective computing
  • signal processing
  • emotion recognition
  • physiological signals
  • multimodal data
  • feature extraction
  • human–computer interaction
  • healthcare
  • mental health
  • artificial intelligence
  • deep learning

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Published Papers (2 papers)

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Research

22 pages, 5294 KiB  
Article
Text-in-Image Enhanced Self-Supervised Alignment Model for Aspect-Based Multimodal Sentiment Analysis on Social Media
by Xuefeng Zhao, Yuxiang Wang and Zhaoman Zhong
Sensors 2025, 25(8), 2553; https://doi.org/10.3390/s25082553 - 17 Apr 2025
Viewed by 239
Abstract
The rapid development of social media has driven the need for opinion mining and sentiment analysis based on multimodal samples. As a fine-grained task within multimodal sentiment analysis, aspect-based multimodal sentiment analysis (ABMSA) enables the accurate and efficient determination of sentiment polarity for [...] Read more.
The rapid development of social media has driven the need for opinion mining and sentiment analysis based on multimodal samples. As a fine-grained task within multimodal sentiment analysis, aspect-based multimodal sentiment analysis (ABMSA) enables the accurate and efficient determination of sentiment polarity for aspect-level targets. However, traditional ABMSA methods often perform suboptimally on social media samples, as the images in these samples typically contain embedded text that conventional models overlook. Such text influences sentiment judgment. To address this issue, we propose a text-in-image enhanced self-supervised alignment model (TESAM) that accounts for multimodal information more comprehensively. Specifically, we employed Optical Character Recognition technology to extract embedded text from images and, based on the principle that text-in-image is an integral part of the visual modality, fused it with visual features to obtain more comprehensive image representations. Additionally, we incorporate aspect words to guide the model in disregarding irrelevant semantic features, thereby reducing noise interference. Furthermore, to mitigate the semantic gap between modalities, we propose pre-training the feature extraction module with self-supervised alignment. During this pre-training stage, unimodal semantic embeddings from both modalities are aligned by calculating errors using Euclidean distance and cosine similarity. Experimental results demonstrate that TESAM achieved remarkable performances on three ABMSA benchmarks. These results validate the rationale and effectiveness of our proposed improvements. Full article
(This article belongs to the Special Issue Advanced Signal Processing for Affective Computing)
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17 pages, 918 KiB  
Article
Fractal Analysis of Electrodermal Activity for Emotion Recognition: A Novel Approach Using Detrended Fluctuation Analysis and Wavelet Entropy
by Luis R. Mercado-Diaz, Yedukondala Rao Veeranki, Edward W. Large and Hugo F. Posada-Quintero
Sensors 2024, 24(24), 8130; https://doi.org/10.3390/s24248130 - 19 Dec 2024
Viewed by 1055
Abstract
The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human–computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) [...] Read more.
The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human–computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) signals. We employed detrended fluctuation analysis (DFA), Hurst exponent estimation, and wavelet entropy calculation to extract fractal features from EDA signals obtained from the CASE dataset, which contains physiological recordings and continuous emotion annotations from 30 participants. The analysis revealed significant differences in fractal features across five emotional states (neutral, amused, bored, relaxed, and scared), particularly those derived from wavelet entropy. A cross-correlation analysis showed robust correlations between fractal features and both the arousal and valence dimensions of emotion, challenging the conventional view of EDA as a predominantly arousal-indicating measure. The application of machine learning for emotion classification using fractal features achieved a leave-one-subject-out accuracy of 84.3% and an F1 score of 0.802, surpassing the performance of previous methods on the same dataset. This study demonstrates the potential of fractal analysis in capturing the intricate, multi-scale dynamics of EDA signals for emotion recognition, opening new avenues for advancing emotion-aware systems and affective computing applications. Full article
(This article belongs to the Special Issue Advanced Signal Processing for Affective Computing)
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