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Advances in Wearable Sensors for Continuous Health Monitoring

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

Deadline for manuscript submissions: 1 May 2026 | Viewed by 10054

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Special Issue Editors

Prof. Dr. Leopoldo Angrisani

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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

Dr. Romano Maria

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Guest Editor

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

Dr. Annarita Tedesco

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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 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-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 (6 papers)

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Research

Jump to: Review, Other

21 pages, 1633 KB

Open AccessArticle

Efficient Deep Learning-Based Arrhythmia Detection Using Smartwatch ECG Electrocardiograms

by Herwin Alayn Huillcen Baca and Flor de Luz Palomino Valdivia

Sensors 2025, 25(17), 5244; https://doi.org/10.3390/s25175244 - 23 Aug 2025

Cited by 5 | Viewed by 4036

Abstract

According to the World Health Organization, cardiovascular diseases, including cardiac arrhythmias, are the leading cause of death worldwide due to their silent, asymptomatic nature. To address this problem, early and accurate diagnosis is crucial. Although this task is typically performed by a cardiologist, diagnosing arrhythmias can be imprecise due to the subjectivity of reading and interpreting electrocardiograms (ECGs), and electrocardiograms are often subject to noise and interference. Deep learning-based approaches present methods for automatically detecting arrhythmias and are positioned as an alternative to support cardiologists’ diagnoses. However, these methods are trained and tested only on open datasets of electrocardiograms from Holter devices, whose results aim to improve the accuracy of the state of the art, neglecting the efficiency of the model and its application in a practical clinical context. In this work, we propose an efficient model based on a 1D CNN architecture to detect arrhythmias from smartwatch ECGs, for subsequent deployment in a practical scenario for the monitoring and early detection of arrhythmias. Two datasets were used: UMass Medical School Simband for a binary arrhythmia detection model to evaluate its efficiency and effectiveness, and the MIT-BIH arrhythmia database to validate the multiclass model and compare it with state-of-the-art models. The results of the binary model achieved an accuracy of 64.81%, a sensitivity of 89.47%, and a specificity of 6.25%, demonstrating the model’s reliability, especially in specificity. Furthermore, the computational complexity was 1.2 million parameters and 68.48 MFlops, demonstrating the efficiency of the model. Finally, the results of the multiclass model achieved an accuracy of 99.57%, a sensitivity of 99.57%, and a specificity of 99.47%, making it one of the best state-of-the-art proposals and also reconfirming the reliability of the model. Full article

(This article belongs to the Special Issue Advances in Wearable Sensors for Continuous Health Monitoring)

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12 pages, 1933 KB

Open AccessArticle

Development of Knitted Strain Sensor Optimized for Dumbbell Exercise and Evaluation of Its Electrical Characteristics

by Hee-Ji Choi and Youn-Hee Kim

Sensors 2025, 25(12), 3685; https://doi.org/10.3390/s25123685 - 12 Jun 2025

Cited by 1 | Viewed by 1182 | Correction

Abstract

With growing interest in wearable technologies, the development of flexible sensors and products that can monitor the human body while being comfortable to wear is gaining momentum. While various textile-based strain sensors have been proposed, their implementation in practical, exercise-specific applications remains limited. In this study, we developed a knitted strain sensor that monitors elbow angles, focusing on dumbbell exercise, which is a basic exercise in sports, and verified its performance. The material of the developed knitted strain sensor with a plain stitch structure comprised a silver-coated nylon conductive yarn and an acrylic/wool blended yarn. To evaluate the electrical and physical characteristics of the developed sensor, a textile folding tester was used to conduct 100 repeated bending experiments at three angles of 30°, 60°, 90° and speeds of 10, 30, 60 cpm. The system demonstrated excellent elasticity, high sensitivity (gauge factor = 698), fast responsiveness, and reliable performance under repeated stress, indicating its potential for integration into wearable fitness or rehabilitation platforms. Full article

(This article belongs to the Special Issue Advances in Wearable Sensors for Continuous Health Monitoring)

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19 pages, 1357 KB

Open AccessArticle

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

Cited by 6 | Viewed by 1929

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 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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Review

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51 pages, 2286 KB

Open AccessReview

Investigation of Heart Rate Variability Indices in Motion Sickness

by Alfonso Maria Ponsiglione, Lorena Guerrini, Simona Pierucci, Vittorio Santoriello, Maria Romano, Marco Recenti, Hannes Petersen, Paolo Gargiulo and Carlo Ricciardi

Sensors 2026, 26(7), 2114; https://doi.org/10.3390/s26072114 - 28 Mar 2026

Viewed by 793

Abstract

Motion sickness (MS), or kinetosis, is a condition experienced by some individuals in response to rhythmic or irregular body motion. Multiple studies have explored its neurobiological mechanisms and countermeasures, with the sensory-conflict hypothesis remaining the most accepted explanation. Heart-rate variability (HRV) and electrocardiography provide complementary autonomic nervous system perspectives that may support MS assessments. From an applied viewpoint, reliable HRV markers could enable the early detection and continuous monitoring of MS in real-world contexts, such as autonomous vehicles, where passenger comfort and safety are critical, motivating contact-free cardiac sensing for unobtrusive monitoring. This systematic review examines the value of HRV indices in MS, conducted under PRISMA guidelines across PubMed, Scopus, and the Web of Science. The included studies were grouped into four categories based on the methods used to induce MS: mechanical stimulus, real trip, visual stimulus, and virtual reality. Aggregated findings indicate that frequency–domain metrics, particularly the low frequency (LF)/high frequency (HF) ratio, HF power, and mean heart rate (mHR), are most frequently reported in relation to MS. Overall, autonomic dysregulation likely contributes to MS susceptibility, but standardized protocols are needed to validate HRV as a reliable marker. Full article

(This article belongs to the Special Issue Advances in Wearable Sensors for Continuous Health Monitoring)

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142 pages, 30152 KB

Open AccessReview

A Systematic Review of Design of Electrodes and Interfaces for Non-Contact and Capacitive Biomedical Measurements: Terminology, Electrical Model, and System Analysis

by Luka Klaić, Dino Cindrić, Antonio Stanešić and Mario Cifrek

Sensors 2026, 26(4), 1374; https://doi.org/10.3390/s26041374 - 22 Feb 2026

Viewed by 773

Abstract

With the advent of ubiquitous healthcare and advancements in textile industry, non-invasive wearable biomedical solutions are becoming an increasingly attractive alternative to in-hospital monitoring, allowing for timely diagnostics and prediction of severe medical conditions. Non-contact biopotential monitoring is particularly promising because non-contact biopotential electrodes can be applied over clothing or embedded in the material without almost any preparation. However, due to the intricacies of capacitive coupling they rely on, the design of such electrodes and their interface with the body plays a key role in achieving measurement repeatability and their widespread utilization in clinical-grade diagnostics. Based on exhaustive investigation of several decades of the literature on non-contact and capacitive biopotential electrodes and electric potential sensors, this study is intended to serve as a state-of-the-art overview of their historical development and design challenges, a collecting point for important research theories and development milestones, a starting point for anyone seeking for a soft head start into this research area, and a remedy for occasional misnomers and conceptual errors identified in the existing papers. The ultimate goal of this comprehensive analysis is to demystify phenomena of non-contact biopotential monitoring and capacitive coupling, systematically reconciliate terminological inconsistencies, and enhance accessibility to the most important findings for future research. To accomplish this, fundamental concepts are thoroughly revisited—from fundamentals of electrochemistry and working principles of capacitors and operational amplifiers to system stability and frequency-domain analysis. With the use of various mathematical tools (Laplace transform, phasors and Fourier analysis, and time-domain differential calculus), discussions on non-contact and capacitive biopotential electrodes, collected from the 1960s onward, are for the first time compiled into a unified, abstracted, bottom-up analysis. The laid-out inspection provides analytical explanation for various aspects of measurement results available in the referenced literature, but also serves an educative purpose by devising a methodological framework that can be easily applied to other similar research fields. Firstly, the differences and similarities between wet, dry, surface-contact, non-contact, capacitive, insulated, on-body, and off-body biopotential electrodes are clarified. For this purpose, equivalent electrical models of various non-invasive biopotential electrodes are analyzed and compared. As a result, a proposal for a revised classification of biopotential electrodes is given. Secondly, instead of using the concept of a purely capacitive biopotential electrode, a test is proposed for assessing the predominant coupling mechanism achieved with an electrode over an insulating layer. Thirdly, a fundamental model of a buffer active non-contact biopotential electrode and its interface with the body is built and generalized, and the proposed test is applied for analyzing the influence of voltage attenuation and phase shifts on signal morphology. Lastly, guidelines for designing the described electrode–body interfaces are proposed, along with a discussion on practical aspects of their implementation. Full article

(This article belongs to the Special Issue Advances in Wearable Sensors for Continuous Health Monitoring)

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