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Sensors for Physiological Monitoring and Digital Health: 2nd Edition

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

Deadline for manuscript submissions: 20 February 2026 | Viewed by 824

Special Issue Editors


E-Mail Website
Guest Editor
School of Engineering, STEM College, RMIT University, Melbourne 3000, Australia
Interests: biomedical engineering; bioelectromagnetics; peptide-based therapeutics; signal processing; bioengineering
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Engineering, STEM College, RMIT University, Melbourne 3000, Australia
Interests: machine learning; signal processing; speech, image and biomedical signal processing and optimisation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Health monitoring that measures and evaluates physiological signals generated by the human body can provide detailed information about human wellness, thus presenting significant potential for personalized healthcare. There is a great need for the long-term monitoring of human vital physiological parameters, such as EEG, ECG, heart rate, etc., for elderly and chronic patients to take care of their health (effectively) and provide treatment during emergencies. Wearable sensors present an exciting opportunity to measure human physiologic parameters in a continuous, real-time, and nonintrusive manner. The market for wearable medical devices has experienced unprecedented growth, with an increase from USD 8.9 billion in 2018 to USD 29.9 billion in 2023. The fast market growth along with advancements in microfabrication, microelectronics, flexible electronics, nanomaterials, wireless communication, and machine learning techniques have led to the evolution of various biosensors and textile-based wearable technologies.

Physiological monitoring with digital health platforms using artificial intelligence (AI) can provide detailed information about health conditions, therefore presenting great potential for personalized healthcare. Digital health monitoring redefines healthcare in multiple ways. It plays a vital role in this transformation, allowing easy access to relevant data, improving quality of care, and delivering value to patients, healthcare practitioners, hospitals, and governments.

In this Special Issue, we want to build a bridge between different scientific disciplines and offer highly innovative researchers in various fields a platform to exchange research in this exciting and emerging field: “Sensors for Physiological Monitoring and Digital Health: 2nd Edition”.

We, the Guest Editors of this Special Issue, represent research backgrounds in biomedical signal processing, health informatics, artificial intelligence, mobility research, and bioinformatics, focusing on biomedical applications and sports science. We stand for the highly interdisciplinary approach that is essential in research in this emerging scientific field and highly anticipate submissions from a broad range of specialties to this Special Issue.

Dr. Ganesh R. Naik
Prof. Dr. Elena Pirogova
Prof. Dr. Margaret Lech
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

  • wearables
  • physiological monitoring
  • digital health
  • healthcare
  • artificial intelligence
  • biomedical signal processing

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Published Papers (1 paper)

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Research

18 pages, 2578 KB  
Article
Emotion Recognition Using Temporal Facial Skin Temperature and Eye-Opening Degree During Digital Content Viewing for Japanese Older Adults
by Rio Tanabe, Ryota Kikuchi, Min Zou, Kenji Suehiro, Nobuaki Takahashi, Hiroki Saito, Takuya Kobayashi, Hisami Satake, Naoko Sato and Yoichi Kageyama
Sensors 2025, 25(21), 6545; https://doi.org/10.3390/s25216545 - 24 Oct 2025
Viewed by 655
Abstract
Electroencephalography is a widely used method for emotion recognition. However, it requires specialized equipment, leading to high costs. Additionally, attaching devices to the body during such procedures may cause physical and psychological stress to participants. These issues are addressed in this study by [...] Read more.
Electroencephalography is a widely used method for emotion recognition. However, it requires specialized equipment, leading to high costs. Additionally, attaching devices to the body during such procedures may cause physical and psychological stress to participants. These issues are addressed in this study by focusing on physiological signals that are noninvasive and contact-free, and a generalized method for estimating emotions is developed. Specifically, the facial skin temperature and eye-opening degree of participants captured via infrared thermography and visible cameras are utilized, and emotional states are estimated while Japanese older adults view digital content. Emotional responses while viewing digital content are often subtle and dynamic. Additionally, various emotions occur during such situations, both positive and negative. Fluctuations in facial skin temperature and eye-opening degree reflect activities in the autonomic nervous system. In particular, expressing emotions through facial expressions is difficult for older adults; as such, emotional estimation using such ecological information is required. Our study results demonstrated that focusing on skin temperature changes and eye movements during emotional arousal and non-arousal using bidirectional long short-term memory yields an F1 score of 92.21%. The findings of this study can enhance emotion recognition in digital content, improving user experience and the evaluation of digital content. Full article
(This article belongs to the Special Issue Sensors for Physiological Monitoring and Digital Health: 2nd Edition)
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