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e-Health Systems and Technologies
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e-Health Systems and Technologies

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 7895

Special Issue Editors

Dr. Tayeb Lemlouma

E-Mail Website
Guest Editor
IRISA CNRS Lab, Univ Rennes, IUT de Lannion, 22300 Lannion, France
Interests: context awareness; pervasive and ubiquitous computing; IoT; e-Health; smart and media services in heterogeneous environments; smart content delivery; content-centric
Special Issues, Collections and Topics in MDPI journals
Dr. Yevgeniya Kovalchuk

E-Mail Website
Guest Editor
Advanced Research Computing Centre, University College London, Gower Street, London WC1E 6BT, UK
Interests: artificial intelligence; machine learning; software engineering; embedded systems; software–hardware integration; sensors and wearables; cyber security; mutli-agent systems; healthcare informatics; movement science and movement and art therapy
Special Issues, Collections and Topics in MDPI journals
Dr. Sébastien Laborie

E-Mail Website
Guest Editor
LIUPPA Lab, University of Pau, 64012 Pau, France
Interests: multimedia documents and applications; adaptation and user profiling; semantic web
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Abderrezak Rachedi

E-Mail Website
Guest Editor
Laboratoire d'Informatique Gaspard Monge, Université Gustave Eiffel, 77454 Marne-la-Vallée, France
Interests: computer network; Internet of Things; AIoT: artificial Intelligent of Things; applied cryptography; blockchain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

Healthcare is a dynamic field that is constantly evolving, with technological advancements playing a significant role in improving the quality of care provided to patients. The emergence of Health Information Technology (health IT), wireless sensor networks, and IoT have contributed to the development of innovative healthcare services that prioritize high-quality, effective, and efficient treatment efforts. Health applications and medical information systems have been established to make treatment proactive, safer, and more affordable, while sensor networks consist of resource-constrained devices that continuously collect an individual's health information and related behavior in real-time. IoT serves as a bridging platform that connects the physical world and cyberspace, allowing for efficient and productive healthcare services and applications.

In light of the economic recession and the COVID-19 pandemic, the need to strengthen health IT data governance, access potential, and interoperability has become even more apparent, and further research is needed to fill the gaps between the quality of service requirements and cost-effective implementation and operation. However, the integration of IoT and sensor networks has proven to be effective in the healthcare industry, improving performance and quality by integrating medical applications and providing high value to patients. These technologies have also been used for various e-Health applications, improving diagnostic and monitoring systems. This presents a unique opportunity to explore the challenges and opportunities presented by this rapidly evolving field, where traditional healthcare systems are unable to meet the needs of a continuously growing and developing society.

For this Special Issue, we invite researchers to submit papers on emerging eHealth applications and medical information systems that assess the impact of various health information technologies on the healthcare industry, both quantitatively and qualitatively. Possible research areas include, but are not limited to telemedicine, telehealth, and telecare; ambient-assisted living and patient empowerment systems; smart sensors for eHealth; clinical decision-making support and smart ePrescription; health data and text mining; artificial intelligence for eHealth; social media and online social networks for healthcare support; mobile healthcare applications; pervasive technologies; personalized medicine; big data and data management; wellness and prevention interventions; evaluation and modeling of healthcare service and mobile app usage; health information exchange and interoperability challenges related to EHRs and patient registries; public health informatics; and population health. We aim to showcase emerging technologies that can revolutionize healthcare and improve the delivery of healthcare services to patients, the elderly, and dependent persons. This Special Issue will be of interest to researchers, healthcare practitioners, and policymakers who are interested in improving the quality of healthcare services through innovative technologies.

If you want to learn more information or need any advice, you can contact the Special Issue Editor Penelope Wang via <penelope.wang@mdpi.com> directly.

Dr. Tayeb Lemlouma
Dr. Yevgeniya Kovalchuk
Dr. Sébastien Laborie
Prof. Dr. Abderrezak Rachedi
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

  • advancements in eHealth technologies for improved healthcare services
  • use of wireless sensor networks and IoT in eHealth
  • application of Artificial Intelligence and machine learning in eHealth
  • cutting-edge technologies such as 5G/6G networks and Thick and Big Data analysis in eHealth
  • security and privacy in eHealth systems
  • user acceptance and quality of experience of eHealth services
  • augmented reality (AR), virtual reality (VR), and mixed reality (MR) in eHealth
  • use of Digital Twin (DT) in eHealth
  • robotic solutions for healthcare in eHealth
  • norms for eHealth data exchange and distribution, such as HL7 and HIE
  • context models for people monitoring and Activities of Daily Living (ADL) in eHealth
  • edge computing for wearable medical devices in eHealth
  • biometric analysis in eHealth
  • delay-tolerant, fault-tolerant, and reliable communication in eHealth
  • security and privacy for eHealth networking and services
  • medical imaging, telemedicine, and IoT for eHealth
  • biomedical and biosensors design in eHealth
  • sensing of vital signs and signatures in eHealth
  • eHealth services/applications for physical and mental health

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

34 pages, 2273 KiB  
Article
SimulatorOrchestrator: A 6G-Ready Simulator for the Cell-Free/Osmotic Infrastructure
by Rohin Gillgallon, Reham Almutairi, Giacomo Bergami and Graham Morgan
Sensors 2025, 25(5), 1591; https://doi.org/10.3390/s25051591 - 5 Mar 2025
Viewed by 703
Abstract
To the best of our knowledge, we offer the first IoT-Osmotic simulator supporting 6G and Cloud infrastructures, leveraging the similarities in Software-Defined Wide Area Network (SD-WAN) architectures when used in Osmotic architectures and User-Centric Cell-Free mMIMO (massive multiple-input multiple-output) architectures. Our simulator acts [...] Read more.
To the best of our knowledge, we offer the first IoT-Osmotic simulator supporting 6G and Cloud infrastructures, leveraging the similarities in Software-Defined Wide Area Network (SD-WAN) architectures when used in Osmotic architectures and User-Centric Cell-Free mMIMO (massive multiple-input multiple-output) architectures. Our simulator acts as a simulator orchestrator, supporting the interaction with a patient digital twin generating patient healthcare data (vital signs and emergency alerts) and a VANET simulator (SUMO), both leading to IoT data streams towards the cloud through pre-initiated MQTT protocols. This contextualises our approach within the healthcare domain while showcasing the possibility of orchestrating different simulators at the same time. The combined provision of these two aspects, joined with the addition of a ring network connecting all the first-mile edge nodes (i.e., access points), enables the definition of new packet routing algorithms, streamlining previous solutions from SD-WAN architectures, thus showing the benefit of 6G architectures in achieving better network load balancing, as well as showcasing the limitations of previous approaches. The simulated 6G architecture, combined with the optimal routing algorithm and MEL (Microelements software components) allocation policy, was able to reduce the time required to route all communications from IoT devices to the cloud by up to 50.4% compared to analogous routing algorithms used within 5G architectures. Full article
(This article belongs to the Special Issue e-Health Systems and Technologies)
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12 pages, 1578 KiB  
Article
Clinical Validation of a Video-Otoscopy-Based Medical Device for the Remote Diagnosis of Ear Complaints
by Ádám Pannonhalmi, Bálint Posta, Ádám Perényi, László Rovó, Balázs Bende, Gábor Katona, Ildikó Csóka, Lajos Kemény and László Szakács
Sensors 2025, 25(3), 758; https://doi.org/10.3390/s25030758 - 27 Jan 2025
Viewed by 883
Abstract
Telemedicine brings several benefits to patients, healthcare providers, and the wider society, including reductions in the need for hospitalizations or readmissions, as well as in overall healthcare costs and the length of inpatient stay. In addition, these services may provide psychological benefits to [...] Read more.
Telemedicine brings several benefits to patients, healthcare providers, and the wider society, including reductions in the need for hospitalizations or readmissions, as well as in overall healthcare costs and the length of inpatient stay. In addition, these services may provide psychological benefits to patients, including excellent satisfaction and medication adherence. The present study aimed to investigate an in-house-developed otorhinolaryngologic remote diagnostic system (mobile app). The basis of the comparison was the incidence between the diagnoses and therapies made by remote diagnosticians and on-site specialists based on static images and videos captured by a smartphone otoscope device. In the study, 103 patients were involved. After registering demographic data, the telemedicine software was evaluated by comparing the matching of physically established diagnoses and/or therapies with remotely established diagnoses and/or therapies. The most remarkable result was in concordance with the diagnoses, with 79 matches identified of the 103 cases examined; the rate of the matching cases was 76.7% (95% CI: 68.5–84.9%). These results support that telemedicine-based otorhinolaryngological remote diagnostics could play a significant role in future healthcare. Full article
(This article belongs to the Special Issue e-Health Systems and Technologies)
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18 pages, 4209 KiB  
Article
Validity Analysis of Monocular Human Pose Estimation Models Interfaced with a Mobile Application for Assessing Upper Limb Range of Motion
by Rayele Moreira, Silmar Teixeira, Renan Fialho, Aline Miranda, Lucas Daniel Batista Lima, Maria Beatriz Carvalho, Ana Beatriz Alves, Victor Hugo Vale Bastos and Ariel Soares Teles
Sensors 2024, 24(24), 7983; https://doi.org/10.3390/s24247983 - 14 Dec 2024
Cited by 1 | Viewed by 1131
Abstract
Human Pose Estimation (HPE) is a computer vision application that utilizes deep learning techniques to precisely locate Key Joint Points (KJPs), enabling the accurate description of a person’s pose. HPE models can be extended to facilitate Range of Motion (ROM) assessment by leveraging [...] Read more.
Human Pose Estimation (HPE) is a computer vision application that utilizes deep learning techniques to precisely locate Key Joint Points (KJPs), enabling the accurate description of a person’s pose. HPE models can be extended to facilitate Range of Motion (ROM) assessment by leveraging patient photographs. This study aims to evaluate and compare the performance of HPE models for assessing upper limbs ROM. A physiotherapist evaluated the degrees of ROM in shoulders (flexion, extension, and abduction) and elbows (flexion and extension) for fifty-two participants using both Universal Goniometer (UG) and five HPE models. Participants were instructed to repeat each movement three times to obtain measurements with the UG, then positioned while photos were captured using the NLMeasurer mobile application. The paired t-test, bias, and error measures were employed to evaluate the difference and agreement between measurement methods. Results indicated that the MoveNet Thunder INT16 model exhibited superior performance. Root Mean Square Errors obtained through this model were <10° in 8 of 10 analyzed movements. HPE models demonstrated better performance in shoulder flexion and abduction movements while exhibiting unsatisfactory performance in elbow flexion. Challenges such as image perspective distortion, environmental lighting conditions, images in monocular view, and complications in the pose may influence the models’ performance. Nevertheless, HPE models show promise in identifying KJPs and facilitating ROM measurements, potentially enhancing convenience and efficiency in assessments. However, their current accuracy for this application is unsatisfactory, highlighting the need for caution when considering automated upper limb ROM measurement with them. The implementation of these models in clinical practice does not diminish the crucial role of examiners in carefully inspecting images and making adjustments to ensure measurement reliability. Full article
(This article belongs to the Special Issue e-Health Systems and Technologies)
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16 pages, 2720 KiB  
Article
eHealth Assistant AI Chatbot Using a Large Language Model to Provide Personalized Answers through Secure Decentralized Communication
by Iuliu Alexandru Pap and Stefan Oniga
Sensors 2024, 24(18), 6140; https://doi.org/10.3390/s24186140 - 23 Sep 2024
Cited by 3 | Viewed by 3840
Abstract
In this paper, we present the implementation of an artificial intelligence health assistant designed to complement a previously built eHealth data acquisition system for helping both patients and medical staff. The assistant allows users to query medical information in a smarter, more natural [...] Read more.
In this paper, we present the implementation of an artificial intelligence health assistant designed to complement a previously built eHealth data acquisition system for helping both patients and medical staff. The assistant allows users to query medical information in a smarter, more natural way, respecting patient privacy and using secure communications through a chat style interface based on the Matrix decentralized open protocol. Assistant responses are constructed locally by an interchangeable large language model (LLM) that can form rich and complete answers like most human medical staff would. Restricted access to patient information and other related resources is provided to the LLM through various methods for it to be able to respond correctly based on specific patient data. The Matrix protocol allows deployments to be run in an open federation; hence, the system can be easily scaled. Full article
(This article belongs to the Special Issue e-Health Systems and Technologies)
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