sensors-logo

Journal Browser

Journal Browser

Special Issue "Wearable and Unobtrusive Technologies for Healthcare Monitoring"

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

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Carlo Massaroni
E-Mail Website
Guest Editor
Unit of Measurement and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
Interests: design of wearable systems for non-invasive measurement of respiratory and cardiac parameters; tests of available technologies for non-invasive measurement in the medical field; fiber optics for development of sensors and measuring chains for medical field physiological monitoring; fiber optic sensors for healthcare and industrial applications
Special Issues and Collections in MDPI journals
Prof. Dr. Emiliano Schena
E-Mail Website
Guest Editor
Laboratory of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
Interests: physiological monitoring; wearable systems; wearable sensors; physiological measurements; active living; cardiorespiratory monitoring; soft sensors
Special Issues and Collections in MDPI journals
Dr. Domenico Formica
E-Mail Website
Guest Editor
Neurophysiology and Neuroengineering of Human-Technology Interaction research unit, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21-00128 Rome, Italy
Interests: neural engineering; wearable sensors; mechatronics for biomedical systems
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable and unobtrusive technologies are revolutionizing personal care services, as well as the screening, prevention, and management of chronic diseases. A range of patients and users may benefit from wearable and unobtrusive technologies for monitoring the progression of the pathologies, facilitating early detection and diagnosis of life-threatening diseases and stress levels, assessing the efficacy of administered therapies, providing low-cost and non-invasive diagnoses, as well as monitoring relevant or vital signals, even remotely.

This Special Issue is focused on wearable sensors and devices, unobtrusive technologies, and applications in the healthcare/wellness fields to improve the safety, effectiveness, and efficiency of healthcare services in acute and chronic conditions, but also for prevention toward a healthy life and active aging. We strongly encourage the submission of papers focusing on the keywords below, but works on related topics may also be considered.

Dr. Carlo Massaroni
Prof. Dr. Emiliano Schena
Dr. Domenico Formica
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 papers will be 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 2200 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 and technologies for medical applications
  • Wearable sensors and technologies for physiological parameter monitoring
  • Wearable and technologies sensors for applications in neuroscience
  • Implantable sensors and devices
  • Environmental sensors and devices for healthcare applications
  • Body area sensor networks for medical applications
  • Sensors for continuous patient monitoring
  • Sensors for remote healthcare applications
  • Metrological assessment of wearable and unobtrusive sensors

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Article
A Feasibility Study of the Use of Smartwatches in Wearable Fall Detection Systems
Sensors 2021, 21(6), 2254; https://doi.org/10.3390/s21062254 - 23 Mar 2021
Viewed by 684
Abstract
Over the last few years, the use of smartwatches in automatic Fall Detection Systems (FDSs) has aroused great interest in the research of new wearable telemonitoring systems for the elderly. In contrast with other approaches to the problem of fall detection, smartwatch-based FDSs [...] Read more.
Over the last few years, the use of smartwatches in automatic Fall Detection Systems (FDSs) has aroused great interest in the research of new wearable telemonitoring systems for the elderly. In contrast with other approaches to the problem of fall detection, smartwatch-based FDSs can benefit from the widespread acceptance, ergonomics, low cost, networking interfaces, and sensors that these devices provide. However, the scientific literature has shown that, due to the freedom of movement of the arms, the wrist is usually not the most appropriate position to unambiguously characterize the dynamics of the human body during falls, as many conventional activities of daily living that involve a vigorous motion of the hands may be easily misinterpreted as falls. As also stated by the literature, sensor-fusion and multi-point measurements are required to define a robust and reliable method for a wearable FDS. Thus, to avoid false alarms, it may be necessary to combine the analysis of the signals captured by the smartwatch with those collected by some other low-power sensor placed at a point closer to the body’s center of gravity (e.g., on the waist). Under this architecture of Body Area Network (BAN), these external sensing nodes must be wirelessly connected to the smartwatch to transmit their measurements. Nonetheless, the deployment of this networking solution, in which the smartwatch is in charge of processing the sensed data and generating the alarm in case of detecting a fall, may severely impact on the performance of the wearable. Unlike many other works (which often neglect the operational aspects of real fall detectors), this paper analyzes the actual feasibility of putting into effect a BAN intended for fall detection on present commercial smartwatches. In particular, the study is focused on evaluating the reduction of the battery life may cause in the watch that works as the core of the BAN. To this end, we thoroughly assess the energy drain in a prototype of an FDS consisting of a smartwatch and several external Bluetooth-enabled sensing units. In order to identify those scenarios in which the use of the smartwatch could be viable from a practical point of view, the testbed is studied with diverse commercial devices and under different configurations of those elements that may significantly hamper the battery lifetime. Full article
(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)
Show Figures

Figure 1

Review

Jump to: Research

Review
Recent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey
Sensors 2021, 21(11), 3814; https://doi.org/10.3390/s21113814 - 31 May 2021
Viewed by 577
Abstract
Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised [...] Read more.
Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised widespread concern about unobtrusive AF detection in routine life. The up-to-date non-invasive AF detection methods include electrocardiogram (ECG) signals and cardiac dynamics signals, such as the ballistocardiogram (BCG) signal, the seismocardiogram (SCG) signal and the photoplethysmogram (PPG) signal. Cardiac dynamics signals can be collected by cushions, mattresses, fabrics, or even cameras, which is more suitable for long-term monitoring. Therefore, methods for AF detection by cardiac dynamics signals bring about extensive attention for recent research. This paper reviews the current unobtrusive AF detection methods based on the three cardiac dynamics signals, summarized as data acquisition and preprocessing, feature extraction and selection, classification and diagnosis. In addition, the drawbacks and limitations of the existing methods are analyzed, and the challenges in future work are discussed. Full article
(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)
Show Figures

Figure 1

Review
A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods
Sensors 2021, 21(11), 3719; https://doi.org/10.3390/s21113719 - 27 May 2021
Viewed by 657
Abstract
The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of [...] Read more.
The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute. Full article
(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)
Show Figures

Figure 1

Back to TopTop