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Sensor and Data Integration, Analysis and Exchange for Healthcare and Healthy Living

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 3842

Special Issue Editors

Dr. Carlos Agostinho

E-Mail Website
Guest Editor
Centre of Technology and Systems, UNINOVA, 2829-516 Caparica, Portugal
Interests: Internet of things; interoperability; knowledge and data management; digital health
Special Issues, Collections and Topics in MDPI journals
Dr. Luigi Gallo

E-Mail Website
Guest Editor
National Research Council of Italy, Institute of High Performance Computing and Networking, ICAR-CNR, 80131 Naples, Italy
Interests: artificial intelligence; human–computer interaction; health informatics

Special Issue Information

Dear Colleagues,

With the increasing availability of sensor technologies and the growing importance of data-driven healthcare solutions, research in this area has gained significant attention in recent years. The proliferation of wearable devices, Internet of things (IoT) sensors, biosensors, and other advanced sensing technologies, alongside efforts in standardized health data exchange (e.g., HL7 FHIR and EEHRxF), has enabled the continuous monitoring of various physiological parameters and lifestyle behaviors. This abundance of data holds immense potential for revolutionizing healthcare delivery by providing clinicians with timely insights into patient health status, facilitating health promotion, early disease detection, and empowering individuals to become active actors in the management of their health and wellbeing.

This Special Issue aims to explore the latest advancements, challenges, and opportunities in sensor and data integration, analysis, and exchange, while addressing application cases in areas such as chronic disease prevention and management, support of ageing populations, healthcare access and affordability, and healthy lifestyle management. Of particular interest are research endeavors delving into the integration of mobile and wearable sensor data with electronic health records and making the most of the utilization of artificial intelligence techniques. This integration aims to establish an interoperable ecosystem that adds value for citizens, patients, caregivers, and medical staff, facilitating continuous behavioral and physiological monitoring both at home, at healthcare facilities, and within communities.

In this Special Issue, original research articles and reviews are welcome.

Dr. Carlos Agostinho
Dr. Luigi Gallo
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

  • smart sensor systems and wearables
  • internet of (medical) things
  • biosensors and systems
  • data management and security
  • data-driven health and care
  • digital health twins
  • health data standards
  • (explainable) artificial intelligence
  • healthcare systems
  • healthy living applications

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Published Papers (3 papers)

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Research

24 pages, 7818 KiB  
Article
Streamlining Sensor Technology: Focusing on Data Fusion and Emotion Evaluation in the e-VITA Project
by Michael McTear, Kristiina Jokinen, Sonja Dana Roelen, Muhammad Saif-Ur-Rehman, Mossaab Hariz, Jérôme Boudy, Christophe Lohr, Florian Szczepaniak, Rainer Wieching and Toshimi Ogawa
Sensors 2025, 25(7), 2217; https://doi.org/10.3390/s25072217 - 1 Apr 2025
Viewed by 496
Abstract
This paper explores the use of sensor-based multimodal data fusion and emotion detection technologies in e-VITA, a three-year EU–Japan collaborative project that developed an AI-powered virtual coaching system to support independent living for older adults. The system integrates these technologies to enable individualized [...] Read more.
This paper explores the use of sensor-based multimodal data fusion and emotion detection technologies in e-VITA, a three-year EU–Japan collaborative project that developed an AI-powered virtual coaching system to support independent living for older adults. The system integrates these technologies to enable individualized profiling and personalized recommendations across multiple domains, including nutrition, physical exercise, sleep, cognition, spirituality, and social health. Following a review of related work, we detail the implementation and evaluation of data fusion and emotion detection in e-VITA. The paper concludes with a summary of the key research findings and directions for future work. Full article
(This article belongs to the Special Issue Sensor and Data Integration, Analysis and Exchange for Healthcare and Healthy Living)
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12 pages, 690 KiB  
Article
Bias Calibration of Optically Pumped Magnetometers Based on Variable Sensitivity
by Jieya Chen, Chaofeng Ye, Xingshen Hou, Yaqiong Niu and Limin Sun
Sensors 2025, 25(2), 433; https://doi.org/10.3390/s25020433 - 13 Jan 2025
Cited by 1 | Viewed by 908
Abstract
Optically pumped magnetometers (OPMs) functioning in the spin-exchange relaxation-free (SERF) regime have emerged as attractive options for measuring weak magnetic fields, owing to their portability and remarkable sensitivity. The operation of SERF-OPM critically relies on the ambient magnetic field; thus, a magnetic field [...] Read more.
Optically pumped magnetometers (OPMs) functioning in the spin-exchange relaxation-free (SERF) regime have emerged as attractive options for measuring weak magnetic fields, owing to their portability and remarkable sensitivity. The operation of SERF-OPM critically relies on the ambient magnetic field; thus, a magnetic field compensation device is commonly employed to mitigate the ambient magnetic field to near zero. Nonetheless, the bias of the OPM may influence the compensation impact, a subject that remains unexamined. This paper introduced an innovative bias calibration technique for OPMs. The sensitivity of the OPM was altered by adjusting the cell temperature. The output of the OPM was then documented with varying sensitivity. It is assumed that the signal exhibits a linear correlation with the environmental magnetic field, and the statistical characteristics of the magnetic field are identical for both measurements, upon which the bias of the OPM is assessed. The bias was subsequently considered in the feedback magnetic field compensation mechanism. The results indicate that this technique might successfully reduce environmental magnetic fluctuations and enhance the sensitivity of the OPM. This technique measured the magnetic field produced by the human heart, confirming the viability of the ultra-weak biomagnetic field measurement approach. Full article
(This article belongs to the Special Issue Sensor and Data Integration, Analysis and Exchange for Healthcare and Healthy Living)
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16 pages, 4036 KiB  
Article
Eye Tracking Post Processing to Detect Visual Artifacts and Quantify Visual Attention under Cognitive Task Activity during fMRI
by Maxime Leharanger, Pan Liu, Luc Vandromme and Olivier Balédent
Sensors 2024, 24(15), 4916; https://doi.org/10.3390/s24154916 - 29 Jul 2024
Viewed by 1763
Abstract
Determining visual attention during cognitive tasks using activation MRI remains challenging. This study aimed to develop a new eye-tracking (ET) post-processing platform to enhance data accuracy, validate the feasibility of subsequent ET-fMRI applications, and provide tool support. Sixteen volunteers aged 18 to 20 [...] Read more.
Determining visual attention during cognitive tasks using activation MRI remains challenging. This study aimed to develop a new eye-tracking (ET) post-processing platform to enhance data accuracy, validate the feasibility of subsequent ET-fMRI applications, and provide tool support. Sixteen volunteers aged 18 to 20 were exposed to a visual temporal paradigm with changing images of objects and faces in various locations while their eye movements were recorded using an MRI-compatible ET system. The results indicate that the accuracy of the data significantly improved after post-processing. Participants generally maintained their visual attention on the screen, with mean gaze positions ranging from 89.1% to 99.9%. In cognitive tasks, the gaze positions showed adherence to instructions, with means ranging from 46.2% to 50%. Temporal consistency assessments indicated prolonged visual tasks can lead to decreased attention during certain tasks. The proposed methodology effectively identified and quantified visual artifacts and losses, providing a precise measure of visual attention. This study offers a robust framework for future work integrating filtered eye-tracking data with fMRI analyses, supporting cognitive neuroscience research. Full article
(This article belongs to the Special Issue Sensor and Data Integration, Analysis and Exchange for Healthcare and Healthy Living)
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