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Advancements in Wearable Sensors for Affective Computing
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Advancements in Wearable Sensors for Affective Computing

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

Deadline for manuscript submissions: closed (21 May 2026) | Viewed by 3147

Special Issue Editors

Dr. Dan Zhang

E-Mail Website
Guest Editor
Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China
Interests: emotional computing; neurophysiological signal; wearable technology
Prof. Dr. Zhao Lv

E-Mail Website
Guest Editor
School of Computer Science and Technology, Anhui University, Hefei 230601, China
Interests: brain-computer interface technology; emotional computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Affective computing, an interdisciplinary field dedicated to recognizing, interpreting, and simulating human emotions, has been significantly advanced by wearable sensor technologies. Modern wearables are now capable of the continuous monitoring of diverse behavioral and physiological signals including gestures, facial expression, EEG, ECG, PPG, GSR, and more, and are transforming how we understand emotional states in real-world daily settings. These developments have ushered in a new era of ubiquitous computing, where emotion-aware systems seamlessly integrate into daily life through ambient intelligence and context-aware architectures. The applications of wearable sensors for affective computing could have profound and far-reaching impacts in fields such as human–computer interaction, mental healthcare, education, and entertainment.

Realizing this vision would demand innovations across multiple frontiers: novel wearable hardware designs for convenient sensing, creative approaches for longitudinal data collection, robust signal processing techniques to overcome real-world noise, and advanced machine learning algorithms that generalize across diverse settings and populations. This Special Issue invites original research articles, reviews, and case studies addressing but not limited to these above challenges.

Dr. Dan Zhang
Prof. Dr. Zhao Lv
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 250 words) can be sent to the Editorial Office for assessment.

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
  • ubiquitous computing
  • wearable sensors
  • physiological signals
  • emotion recognition
  • daily life
  • machine learning

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Published Papers (2 papers)

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Research

23 pages, 1863 KB  
Article
Real-Time Pain Assessment from Electrodermal Activity Using Deep Learning
by Calvin Joseph, Maryam Ghahramani and Raul Fernandez Rojas
Sensors 2026, 26(10), 3020; https://doi.org/10.3390/s26103020 - 11 May 2026
Viewed by 394
Abstract
Objective pain assessment remains a significant challenge in clinical and research settings due to the subjective nature of self-reported measures. Physiological signals, particularly electrodermal activity (EDA), have emerged as promising indicators of autonomic responses associated with pain. Although recent advances in deep learning [...] Read more.
Objective pain assessment remains a significant challenge in clinical and research settings due to the subjective nature of self-reported measures. Physiological signals, particularly electrodermal activity (EDA), have emerged as promising indicators of autonomic responses associated with pain. Although recent advances in deep learning have improved the modelling of complex biosignals, many existing approaches remain computationally demanding, limiting their applicability for real-time monitoring in wearable and embedded systems. This paper proposes a fully convolutional network (FCN) for automated pain recognition using EDA signals. The proposed model is designed to efficiently capture temporal patterns in physiological data while maintaining low computational complexity. The approach is evaluated on the AI4Pain dataset for three-class pain classification (No Pain, Low Pain, High Pain). Experimental results show that the proposed FCN achieves an accuracy of 79.23% in offline evaluation. Furthermore, the model enables real-time inference with a latency of 0.47 ms, achieving 73.14% accuracy during real-time operation. These results demonstrate that convolutional architectures can provide an effective balance between predictive performance and computational efficiency, supporting the development of real-time physiological pain monitoring systems using wearable sensing technologies. Full article
(This article belongs to the Special Issue Advancements in Wearable Sensors for Affective Computing)
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16 pages, 1434 KB  
Article
Utilizing Tympanic Membrane Temperature for Earphone-Based Emotion Recognition
by Kaita Furukawa, Xinyu Shui, Ming Li and Dan Zhang
Sensors 2025, 25(14), 4411; https://doi.org/10.3390/s25144411 - 15 Jul 2025
Cited by 2 | Viewed by 1745
Abstract
Emotion recognition by wearable devices is essential for advancing emotion-aware human–computer interaction in real life. Earphones have the potential to naturally capture brain activity and its lateralization, which is associated with emotion. In this study, we newly introduced tympanic membrane temperature (TMT), previously [...] Read more.
Emotion recognition by wearable devices is essential for advancing emotion-aware human–computer interaction in real life. Earphones have the potential to naturally capture brain activity and its lateralization, which is associated with emotion. In this study, we newly introduced tympanic membrane temperature (TMT), previously used as an index of lateralized brain activation, for earphone-based emotion recognition. We developed custom earphones to measure bilateral TMT and conducted two experiments consisting of emotion induction by autobiographical recall and scenario imagination. Using features derived from the right–left TMT difference, we trained classifiers for both four-class discrete emotion and valence (positive vs. negative) classification tasks. The classifiers achieved 36.2% and 42.5% accuracy for four-class classification and 72.5% and 68.8% accuracy for binary classification, respectively, in the two experiments, confirmed by leave-one-participant-out cross-validation. Notably, consistent improvement in accuracy was specific to models utilizing right–left TMT and not observed in models utilizing the right–left wrist skin temperature. These findings suggest that lateralization in TMT provides unique information about emotional state, making it valuable for emotion recognition. With the ease of measurement by earphones, TMT has significant potential for real-world application of emotion recognition. Full article
(This article belongs to the Special Issue Advancements in Wearable Sensors for Affective Computing)
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