Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Wearables Technology for COVID-19
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Wearables Technology for COVID-19

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

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 5162

Special Issue Editors

Dr. Yehuda Weizman

E-Mail Website
Guest Editor
Faculty of Engineering Science, University of Bayreuth, D-95440 Bayreuth, Germany
Interests: sensors; smart equipment; wearable technology; sports engineering; data analytics product innovation; electronics design; gait analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Franz Konstantin Fuss

E-Mail Website
Guest Editor
1. Chair of Biomechanics, Faculty of Engineering Science, University of Bayreuth, 95447 Bayreuth, Germany
2. Division of Biomechanics, Department of Biomechatronic Systems, Fraunhofer Institute of Manufacturing Engineering and Automation IPA, 70569 Stuttgart, Germany
Interests: bioengineering; sports engineering; sports technology; implant engineering; biomechanics; smart equipment; wearable electronics; sensors; signal processing; non-linear engineering

Special Issue Information

Dear Colleagues,

As the recent COVID-19 pandemic has driven technology forward, numerous studies have used different wearable technologies to monitor, predict, mitigate, and prevent COVID-19 in the past 3 years. The use of wearable sensors has proven to be an essential clinical tool that provided unique insights into COVID-19 management and prevention. These devices become more and more capable of collecting accurate data together with increasing computing capabilities, such as AI and machine learning. Now, more than ever, wearable technologies provide a fertile ground for countless applications in public health, sedentary behavior, remote clinical monitoring, and digital healthcare, in response to COVID-19.

This Special Issue is intended to report and explore recent approaches for health and wellbeing applications that were made during COVID-19. We welcome research studies, as well as review manuscripts focusing on the application of wearables for the objective recognition which might include, but are not limited to, the following topics:

  • Remote clinical monitoring;
  • Digital healthcare;
  • Public health;
  • Sedentary behavior;
  • Well-being;
  • Physiological disorders;
  • Detection;
  • Prevention;
  • Mitigation;
  • AI;
  • IOT;
  • IMU;
  • Machine learning.

Dr. Yehuda Weizman
Prof. Dr. Franz Konstantin Fuss
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

  • remote clinical monitoring
  • digital healthcare
  • public health
  • sedentary behavior
  • well-being
  • physiological disorders
  • detection
  • prevention
  • mitigation
  • AI
  • IOT
  • IMU
  • machine learning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

15 pages, 2029 KiB  
Article
Smartphone Technology to Remotely Measure Postural Sway during Double- and Single-Leg Squats in Adults with Femoroacetabular Impingement and Those with No Hip Pain
by Charlotte J. Marshall, Charlotte Ganderton, Adam Feltham, Doa El-Ansary, Adrian Pranata, John O’Donnell, Amir Takla, Phong Tran, Nilmini Wickramasinghe and Oren Tirosh
Sensors 2023, 23(11), 5101; https://doi.org/10.3390/s23115101 - 26 May 2023
Cited by 2 | Viewed by 2198
Abstract
Background: The COVID-19 pandemic has accelerated the demand for utilising telehealth as a major mode of healthcare delivery, with increasing interest in the use of tele-platforms for remote patient assessment. In this context, the use of smartphone technology to measure squat performance in [...] Read more.
Background: The COVID-19 pandemic has accelerated the demand for utilising telehealth as a major mode of healthcare delivery, with increasing interest in the use of tele-platforms for remote patient assessment. In this context, the use of smartphone technology to measure squat performance in people with and without femoroacetabular impingement (FAI) syndrome has not been reported yet. We developed a novel smartphone application, the TelePhysio app, which allows the clinician to remotely connect to the patient’s device and measure their squat performance in real time using the smartphone inertial sensors. The aim of this study was to investigate the association and test–retest reliability of the TelePhysio app in measuring postural sway performance during a double-leg (DLS) and single-leg (SLS) squat task. In addition, the study investigated the ability of TelePhysio to detect differences in DLS and SLS performance between people with FAI and without hip pain. Methods: A total of 30 healthy (nfemales = 12) young adults and 10 adults (nfemales = 2) with diagnosed FAI syndrome participated in the study. Healthy participants performed DLS and SLS on force plates in our laboratory, and remotely in their homes using the TelePhysio smartphone application. Sway measurements were compared using the centre of pressure (CoP) and smartphone inertial sensor data. A total of 10 participants with FAI (nfemales = 2) performed the squat assessments remotely. Four sway measurements in each axis (x, y, and z) were computed from the TelePhysio inertial sensors: (1) average acceleration magnitude from the mean (aam), (2) root-mean-square acceleration (rms), (3) range acceleration (r), and (4) approximate entropy (apen), with lower values indicating that the movement is more regular, repetitive, and predictable. Differences in TelePhysio squat sway data were compared between DLS and SLS, and between healthy and FAI adults, using analysis of variance with significance set at 0.05. Results: The TelePhysio aam measurements on the x- and y-axes had significant large correlations with the CoP measurements (r = 0.56 and r = 0.71, respectively). The TelePhysio aam measurements demonstrated moderate to substantial between-session reliability values of 0.73 (95% CI 0.62–0.81), 0.85 (95% CI 0.79–0.91), and 0.73 (95% CI 0.62–0.82) for aamx, aamy, and aamz, respectively. The DLS of the FAI participants showed significantly lower aam and apen values in the medio-lateral direction compared to the healthy DLS, healthy SLS, and FAI SLS groups (aam = 0.13, 0.19, 0.29, and 0.29, respectively; and apen = 0.33, 0.45, 0.52, and 0.48, respectively). In the anterior–posterior direction, healthy DLS showed significantly greater aam values compared to the healthy SLS, FAI DLS, and FAI SLS groups (1.26, 0.61, 0.68, and 0.35, respectively). Conclusions: The TelePhysio app is a valid and reliable method of measuring postural control during DLS and SLS tasks. The application is capable of distinguishing performance levels between DLS and SLS tasks, and between healthy and FAI young adults. The DLS task is sufficient to distinguish the level of performance between healthy and FAI adults. This study validates the use of smartphone technology as a tele-assessment clinical tool for remote squat assessment. Full article
(This article belongs to the Special Issue Wearables Technology for COVID-19)
►▼ Show Figures

Figure 1

Other

Jump to: Research

21 pages, 763 KiB  
Systematic Review
The Use of Wearable Devices to Measure Sedentary Behavior during COVID-19: Systematic Review and Future Recommendations
by Yehuda Weizman, Adin Ming Tan and Franz Konstantin Fuss
Sensors 2023, 23(23), 9449; https://doi.org/10.3390/s23239449 - 27 Nov 2023
Viewed by 1595
Abstract
The SARS-CoV-2 pandemic resulted in approximately 7 million deaths and impacted 767 million individuals globally, primarily through infections. Acknowledging the impactful influence of sedentary behaviors, particularly exacerbated by COVID-19 restrictions, a substantial body of research has emerged, utilizing wearable sensor technologies to assess [...] Read more.
The SARS-CoV-2 pandemic resulted in approximately 7 million deaths and impacted 767 million individuals globally, primarily through infections. Acknowledging the impactful influence of sedentary behaviors, particularly exacerbated by COVID-19 restrictions, a substantial body of research has emerged, utilizing wearable sensor technologies to assess these behaviors. This comprehensive review aims to establish a framework encompassing recent studies concerning wearable sensor applications to measure sedentary behavior parameters during the COVID-19 pandemic, spanning December 2019 to December 2022. After examining 582 articles, 7 were selected for inclusion. While most studies displayed effective reporting standards and adept use of wearable device data for their specific research aims, our inquiry revealed deficiencies in apparatus accuracy documentation and study methodology harmonization. Despite methodological variations, diverse metrics, and the absence of thorough device accuracy assessments, integrating wearables within the pandemic context offers a promising avenue for objective measurements and strategies against sedentary behaviors. Full article
(This article belongs to the Special Issue Wearables Technology for COVID-19)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop