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Advances in Wearable Sensors for Continuous Health Monitoring

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

Deadline for manuscript submissions: 30 October 2025 | Viewed by 356

Special Issue Editors


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Guest Editor
Department of Information Technology and Electrical Engineering, University of Napoli Federico II, Naples, Italy
Interests: measurement in the IoT field and, more generally, in the Industry 4.0 and Health 4.0 fields; cyber-physical measurement systems; measurement of ICT systems sustainability and sustainability of measurements; operation and performance assessment of communication systems, equipment, and networks; measurement uncertainty; impact of quantum technologies on measurements; metrological characterization of advanced human-to-machine interfaces
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electrical Engineering and Information Technology, 80138 Naples, Italy
Interests: gait analysis; gait monitoring; wearable sensor

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Guest Editor
Department of Public Health, University of Napoli Federico II, Naples, Italy
Interests: health 4.0; new digital technologies for health monitoring; ICT sustainability in biomedical applications; advanced human-to-machine interfaces
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable technologies and their seamless integration with 5.0 health infrastructure are contributing to the implementation of a patient-centered approach. Wearable technologies allow for personalized healthcare interventions, the early detection and management of diseases, and the monitoring of chronic conditions, enabling improved health outcomes and quality of life. In addition, the introduction of advanced machine learning technologies has increased the potential of personalized medicine by allowing the handling and processing the large amounts of data received from pervasive wearable sensors.

Starting from these considerations, this Special Issue requests contributions on the current state of the art in the field of wearable technologies for biomedical applications, including advancements in sensor technology, data processing and analytics, the integration into healthcare systems, disease management, and remote patient monitoring.

Topics of interest include, but are not limited to, the following:

  • Design, manufacturing, and fabrication of advanced sensors for healthcare applications;
  • AI-enabled based healthcare framework with wearable sensors;
  • Wearable sensors with anomaly detection;
  • Design, implementation, and test of novel sensing principles;
  • Machine learning/deep learning techniques for real-time wearable sensor data analytics;
  • Distributed and connected sensing using wearable sensors;
  • Remote health monitoring with wearable sensors;
  • Signal processing and data collection through wearable sensors;
  • Algorithms and tools for sensing and disease prediction with advanced wearable sensors;

Medical data transmission, acquisition, and integration with wearable sensors.

Prof. Dr. Leopoldo Angrisani
Prof. Dr. Romano Maria
Dr. Annarita Tedesco
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

  • wearable sensors
  • patient monitoring
  • health management
  • remote health monitoring
  • AI-enabled processing
  • medical IoT

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Published Papers (1 paper)

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Research

19 pages, 1357 KiB  
Article
Performance Measurement of Gesture-Based Human–Machine Interfaces Within eXtended Reality Head-Mounted Displays
by Leopoldo Angrisani, Mauro D’Arco, Egidio De Benedetto, Luigi Duraccio, Fabrizio Lo Regio, Michele Sansone and Annarita Tedesco
Sensors 2025, 25(9), 2831; https://doi.org/10.3390/s25092831 - 30 Apr 2025
Viewed by 197
Abstract
This paper proposes a method for measuring the performance of Human–Machine Interfaces based on hand-gesture recognition, implemented within eXtended Reality Head-Mounted Displays. The proposed method leverages a systematic approach, enabling performance measurement in compliance with the Guide to the Expression of Uncertainty in [...] Read more.
This paper proposes a method for measuring the performance of Human–Machine Interfaces based on hand-gesture recognition, implemented within eXtended Reality Head-Mounted Displays. The proposed method leverages a systematic approach, enabling performance measurement in compliance with the Guide to the Expression of Uncertainty in Measurement. As an initial step, a testbed is developed, comprising a series of icons accommodated within the field of view of the eXtended Reality Head-Mounted Display considered. Each icon must be selected through a cue-guided task using the hand gestures under evaluation. Multiple selection cycles involving different individuals are conducted to derive suitable performance metrics. These metrics are derived considering the specific parameters characterizing the hand gestures, as well as the uncertainty contributions arising from intra- and inter-individual variability in the measured quantity values. As a case study, the eXtended Reality Head-Mounted Display Microsoft HoloLens 2 and the finger-tapping gesture were investigated. Without compromising generality, the obtained results show that the proposed method can provide valuable insights into performance trends across individuals and gesture parameters. Moreover, the statistical analyses employed can determine whether increased individual familiarity with the Human–Machine Interface results in faster task completion without a corresponding decrease in accuracy. Overall, the proposed method provides a comprehensive framework for evaluating the compliance of hand-gesture-based Human–Machine Interfaces with target performance specifications related to specific application contexts. Full article
(This article belongs to the Special Issue Advances in Wearable Sensors for Continuous Health Monitoring)
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