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Multimodal Ubiquitous Sensing for Human-Centered Healthcare
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Multimodal Ubiquitous Sensing for Human-Centered Healthcare

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

Deadline for manuscript submissions: 30 December 2026 | Viewed by 979

Editors

Dr. Yao Ge

E-Mail Website
Guest Editor
James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Interests: wireless rf sensing; artificial intelligence and smart healthcare systems
Dr. Qammer Abbasi

E-Mail Website
Guest Editor
James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Interests: nano communication; biomedical applications of millimeter and terahertz communication; wearable and flexible sensors; compact antenna design; RF design and radio propagation; antenna interaction with human body; implants; body centric wireless communication issues; wireless body sensor networks; non-invasive health care solutions; physical layer security for wearable/implant communication and multiple-input–multiple-output systems
Special Issues, Collections and Topics in MDPI journals
Dr. Wenda Li

E-Mail Website
Guest Editor
School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK
Interests: passive radar; activity recognition; remote sensing; ISAC; digital health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, titled "Multimodal Ubiquitous Sensing for Human-Centered Healthcare," invites significant contributions that are advancing the landscape of modern healthcare through the integration of diverse and pervasive sensing technologies. We are moving beyond conventional healthcare paradigms to embrace a holistic approach that not only identifies critical events but also understands and preempts health and wellness states through continuous, unobtrusive monitoring.

We encourage the submission of original research and review articles that explore the fusion of data from various sensors—such as wearable, ambient, and mobile devices—to create a comprehensive understanding of human well-being. This Special Issue places a strong emphasis on a broader spectrum of healthcare applications. We seek to highlight innovations that extend beyond the well-established domain of recognizing overt danger, such as falls or accidents. We are particularly interested in pioneering work on the detection of subtle vital signs, including but not limited to non-contact respiratory and cardiac monitoring, and the analysis of minute physiological changes that may indicate incipient health issues.

Furthermore, a key focus of this issue will be on the burgeoning field of advanced mental health sensing. We welcome manuscripts that discuss novel methodologies for the objective assessment of mental and emotional states through the analysis of behavioral, physiological, and social cues captured by ubiquitous sensors. Topics of interest include, but are not limited to, stress and anxiety detection, mood tracking, and the identification of early markers for psychological distress.

Dr. Yao Ge
Dr. Qammer Abbasi
Dr. Wenda Li
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-anonymized 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

  • multimodal sensing
  • ubiquitous healthcare
  • wearable devices
  • non-contact monitoring
  • continuous health tracking
  • vital signs detection

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Published Papers (1 paper)

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Research

26 pages, 18614 KB  
Article
Sensor-Modality-Aware Human Activity Recognition with the Convolutional Tsetlin Machine: Interpretable and Resource-Efficient Neuro-Symbolic Learning
by Olga Tarasyuk, Anatoliy Gorbenko, Oleksandr Gordieiev, Artem Akulynichev, Rishad Shafik and Alex Yakovlev
Sensors 2026, 26(14), 4482; https://doi.org/10.3390/s26144482 - 15 Jul 2026
Viewed by 460
Abstract
Human activity recognition (HAR) based on smartphone and wearable sensor data is commonly addressed using statistical learning methods and deep neural networks that often provide strong predictive performance, but at the expense of limited interpretability and substantial computational and energy requirements. Such limitations [...] Read more.
Human activity recognition (HAR) based on smartphone and wearable sensor data is commonly addressed using statistical learning methods and deep neural networks that often provide strong predictive performance, but at the expense of limited interpretability and substantial computational and energy requirements. Such limitations reduce their suitability for deployment in practical sensing environments where model decisions must be transparent, verifiable and executable on resource-constrained devices. In this work, we investigate the Convolutional Tsetlin Machine (CTM) for multimodal HAR using only the raw inertial signals (9 × 128) of the UCI-HAR dataset, rather than its pre-computed 561-feature representation. The Tsetlin Machine is a novel neuro-symbolic machine learning approach that offers two important advantages over many conventional machine learning methods: (i) it learns logic-based decision rules that support human inspection and provide a transparent basis for analyzing model decisions, and (ii) it operates with comparatively low computational complexity, making it well suited to efficient and low-power on-device learning. The proposed study systematically analyses the contribution of different feature modalities by decomposing the inertial signals space into semantically defined subsets according to: (i) sensor source: accelerometer and gyroscope; (ii) signal group: gyroscope angular velocity, body and total acceleration (including gravity); (iii) coordinate axis: x, y and z. A separate CTM classifier was trained for each modality and its combinations in order to determine the relative discriminative value of each modality group for activity classification. In addition to predictive performance, the study emphasizes the interpretability of the CTM model ensured by expressing each decision in the form of propositional clauses, thereby enabling visualization and direct inspection of the modality-specific patterns supporting each activity class. Owing to its symbolic structure and modest computational demands, the CTM provides a principled framework for the design of explainable, resource-efficient and deployable HAR systems. The proposed work therefore contributes toward trustworthy multimodal sensing by jointly addressing predictive performance, interpretability and suitability for embedded and mobile platforms. Full article
(This article belongs to the Special Issue Multimodal Ubiquitous Sensing for Human-Centered Healthcare)
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