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Diagnostics
Special Issues
Wearable Sensors for Health Monitoring and Diagnostics
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Wearable Sensors for Health Monitoring and Diagnostics

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Point-of-Care Diagnostics and Devices".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 3135

Special Issue Editors

Dr. Leandro Donisi

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Guest Editor
Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, Piazza Luigi Miraglia, 2, 80138 Naples, Italy
Interests: biomedical engineering; biosignal and bioimage processing; ergonomics; rehabilitation engineering, gait analysis, wearable sensors; telemedicine; machine learning; biostatistics
Special Issues, Collections and Topics in MDPI journals
Dr. Carlo Ricciardi

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy
Interests: machine learning; statistics; gait analysis; health technology assessment; lean six sigma; biomedical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Alfonso Maria Ponsiglione

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80125 Naples, Italy
Interests: nanomedicine; microfluidics; drug delivery systems; biomaterials
Special Issues, Collections and Topics in MDPI journals
Dr. Giuseppe Cesarelli

E-Mail Website
Guest Editor
Department of Chemical, Materials and Production Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy
Interests: biomedical engineering; bioengineering; biomedical data analysis; biomedical signal processing; drug delivery systems; biomaterials; polymer microparticles; lean six sigma in healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of wearable sensors capable of collecting a wide variety of relevant physiological and environmental parameters allows for the acquisition of worker-related signals in a non-intrusive, automatic, and continuous way. Data can be obtained through both custom-made devices (namely, ad hoc ones developed by scientific researchers) and commercial wearable devices. The availability of instruments (such as wearable motion trackers, inertial measurement units, pressure sensors, eye and facial expression tracking devices, smart sensors for temperature, breathing, electrocardiography, electroencephalography, electromyography, and electrodermal activity) offers many opportunities for novel solutions in health monitoring and diagnosis.

Consequently, this Special Issue aims to delineate an emerging branch of science that considers wearable sensors a tool for biomechanical risk assessment and injury prevention, even with the help of artificial intelligence, during work-, home-, sport-, and leisure-related activities. We welcome submissions on the design of novel sensors or commercial wearable technologies and the development of any novel methodology aiming to integrate quantitative physiological information, with and without the use of artificial intelligence, to achieve the main goals of health monitoring and diagnosis. Both research papers and review articles will be considered.

The topics of interest include, but are not limited to, the following fields:

  • Ergonomics and occupational medicine;
  • Wearable, motion, force/pressure, and EMG sensors for ergonomics;
  • Sensors for well-being;
  • Activity-monitoring devices and systems;
  • Machine learning and deep learning for wearable data analysis;
  • Biomechanical risk assessment;
  • Health monitoring in working environments;
  • Work-related musculoskeletal disorders;
  • Novel design approaches for ergonomic assessment;
  • M-health and/or e-health solutions for ergonomics.

Dr. Leandro Donisi
Dr. Carlo Ricciardi
Dr. Alfonso Maria Ponsiglione
Dr. Giuseppe Cesarelli
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. Diagnostics 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
  • health monitoring
  • diagnostics
  • ergonomics and occupational medicine
  • machine learning and deep learning

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Published Papers (3 papers)

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Research

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13 pages, 1651 KiB  
Article
Evaluation of Non-Invasive Hemoglobin Monitoring in Perioperative Patients: A Retrospective Study of the Rad-67TM (Masimo)
by Philipp Helmer, Andreas Steinisch, Sebastian Hottenrott, Tobias Schlesinger, Michael Sammeth, Patrick Meybohm and Peter Kranke
Diagnostics 2025, 15(2), 128; https://doi.org/10.3390/diagnostics15020128 - 8 Jan 2025
Viewed by 917
Abstract
Background: Hemoglobin (Hb) is a crucial parameter in perioperative care due to its essential role for oxygen transport and tissue oxygenation. Accurate Hb monitoring allows for timely interventions to address perioperative anemia and, thus, prevent morbidity and mortality. Traditional Hb measurements rely [...] Read more.
Background: Hemoglobin (Hb) is a crucial parameter in perioperative care due to its essential role for oxygen transport and tissue oxygenation. Accurate Hb monitoring allows for timely interventions to address perioperative anemia and, thus, prevent morbidity and mortality. Traditional Hb measurements rely on invasive blood sampling, which significantly contributes to iatrogenic anemia and poses discomfort and increased infection risks. The advent of non-invasive devices like Masimo’s Rad-67™, which measures Hb using pulse CO-oximetry (SpHb), offers a promising alternative. This study evaluates the accuracy of SpHb compared to clinical standard blood gas analysis (BGA) in perioperative patients. Methods: This retrospective study analyzed 335 paired Hb measurements with an interval <15 min between SpHb and BGA in the operating theater and post-anesthesia care unit of a university hospital. Patients experiencing hemodynamic instability, acute bleeding, or critical care were excluded. Statistical analysis included Bland–Altman plots and Pearson correlation coefficients (PCCs) to assess the agreement between SpHb and BGA. Potential confounders, e.g., patient age, skin temperature, sex, perfusion index (PI), and atrial fibrillation, were also analyzed. Results: The bias of the SpHb compared to BGA according to Bland–Altman was 0.00 g/dL, with limits of agreement ranging from −2.70 to 2.45 g/dL. A strong correlation was observed (r = 0.79). Overall, 57.6% of the paired measurements showed a deviation between the two methods of ≤±1 g/dL; however, this applied to only 33.3% of the anemic patients. Modified Clark’s Error Grid analysis showed 85.4% of values fell within clinically acceptable limits. Sex was found to have a statistically significant, but not clinically relevant, effect on accuracy (p = 0.02). Conclusions: The Rad-67TM demonstrates reasonable accuracy for non-invasive SpHb, but exhibits significant discrepancies in anemic patients with overestimating low values. While it offers potential for reducing iatrogenic blood loss, SpHb so far should not replace BGA in critical clinical decision-making. Full article
(This article belongs to the Special Issue Wearable Sensors for Health Monitoring and Diagnostics)
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Review

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69 pages, 1033 KiB  
Review
Biosensors, Artificial Intelligence Biosensors, False Results and Novel Future Perspectives
by Georgios Goumas, Efthymia N. Vlachothanasi, Evangelos C. Fradelos and Dimitra S. Mouliou
Diagnostics 2025, 15(8), 1037; https://doi.org/10.3390/diagnostics15081037 - 18 Apr 2025
Viewed by 515
Abstract
Medical biosensors have set the basis of medical diagnostics, and Artificial Intelligence (AI) has boosted diagnostics to a great extent. However, false results are evident in every method, so it is crucial to identify the reasons behind a possible false result in order [...] Read more.
Medical biosensors have set the basis of medical diagnostics, and Artificial Intelligence (AI) has boosted diagnostics to a great extent. However, false results are evident in every method, so it is crucial to identify the reasons behind a possible false result in order to control its occurrence. This is the first critical state-of-the-art review article to discuss all the commonly used biosensor types and the reasons that can give rise to potential false results. Furthermore, AI is discussed in parallel with biosensors and their misdiagnoses, and again some reasons for possible false results are discussed. Finally, an expert opinion with further future perspectives is presented based on general expert insights, in order for some false diagnostic results of biosensors and AI biosensors to be surpassed. Full article
(This article belongs to the Special Issue Wearable Sensors for Health Monitoring and Diagnostics)
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Other

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24 pages, 2050 KiB  
Systematic Review
Validity of Wearable Inertial Sensors for Gait Analysis: A Systematic Review
by Giuseppe Prisco, Maria Agnese Pirozzi, Antonella Santone, Fabrizio Esposito, Mario Cesarelli, Francesco Amato and Leandro Donisi
Diagnostics 2025, 15(1), 36; https://doi.org/10.3390/diagnostics15010036 - 27 Dec 2024
Cited by 3 | Viewed by 1885
Abstract
Background/Objectives: Gait analysis, traditionally performed with lab-based optical motion capture systems, offers high accuracy but is costly and impractical for real-world use. Wearable technologies, especially inertial measurement units (IMUs), enable portable and accessible assessments outside the lab, though challenges with sensor placement, [...] Read more.
Background/Objectives: Gait analysis, traditionally performed with lab-based optical motion capture systems, offers high accuracy but is costly and impractical for real-world use. Wearable technologies, especially inertial measurement units (IMUs), enable portable and accessible assessments outside the lab, though challenges with sensor placement, signal selection, and algorithm design can affect accuracy. This systematic review aims to bridge the benchmarking gap between IMU-based and traditional systems, validating the use of wearable inertial systems for gait analysis. Methods: This review examined English studies between 2012 and 2023, retrieved from the Scopus database, comparing wearable sensors to optical motion capture systems, focusing on IMU body placement, gait parameters, and validation metrics. Exclusion criteria for the search included conference papers, reviews, unavailable papers, studies without wearable inertial sensors for gait analysis, and those not involving agreement studies or optical motion capture systems. Results: From an initial pool of 479 articles, 32 were selected for full-text screening. Among them, the lower body resulted in the most common site for single IMU placement (in 22 studies), while the most frequently used multi-sensor configuration involved IMU positioning on the lower back, shanks, feet, and thighs (10 studies). Regarding gait parameters, 11 studies out of the 32 included studies focused on spatial-temporal parameters, 12 on joint kinematics, 2 on gait events, and the remainder on a combination of parameters. In terms of validation metrics, 24 studies employed correlation coefficients as the primary measure, while 7 studies used a combination of error metrics, correlation coefficients, and Bland–Altman analysis. Validation metrics revealed that IMUs exhibited good to moderate agreement with optical motion capture systems for kinematic measures. In contrast, spatiotemporal parameters demonstrated greater variability, with agreement ranging from moderate to poor. Conclusions: This review highlighted the transformative potential of wearable IMUs in advancing gait analysis beyond the constraints of traditional laboratory-based systems. Full article
(This article belongs to the Special Issue Wearable Sensors for Health Monitoring and Diagnostics)
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