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Wearables and IoT Sensors for Applications in Healthcare
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Wearables and IoT Sensors for Applications in Healthcare

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

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 24940

Special Issue Editors

Prof.Dr. Hans Hallez

E-Mail Website
Guest Editor
Mechatronics Group, Department of Computer Science, KU Leuven,3000 Leuven, Belgium
Interests: sensors; internet of things; software frameworks for sensor ecosystems
Prof. Dr. Bart Vanrumste

E-Mail Website
Guest Editor
E-MEDIA, STADIUS, Department of Electrical Engineering (ESAT), Campus Group T, KU Leuven, 3000 Leuven, Belgium
Interests: biomedical signal analysis; blind source separation; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wei Chen

E-Mail Website
Guest Editor
School of Information Science and Technology, Fudan University, Shanghai 200433, China
Interests: medical monitoring system; patient health monitoring; neonatal monitoring; brain activity monitoring; smart sleep; smart rehabilitation system; wireless body area networks Photo:
Special Issues, Collections and Topics in MDPI journals
Dr. Roozbeh Jafari

E-Mail Website
Guest Editor
Texas A&M University, College Station, United States
Interests: Wearable Sensors; Signal Processing; Embedded Systems

Special Issue Information

Dear Colleagues,

The introduction of sensors and wearables in medicine have been beneficial. Sensors are able to collect data which can be analyzed. They can provide care personnel with invaluable insights into symptoms or the trends in wellbeing in healthcare. Using recommendations and remote care, healthcare provisioning can be optimized in order to increase efficiency in terms of costs and personnel allocation. This Special Issue will address all novel sensor systems consisting of wearable sensors, signal processing techniques, and decision support systems for healthcare applications.

Prof. Hans Hallez
Prof. Bart Vanrumste
Prof. Dr. Chen Wei
Dr. Roozbeh Jafari
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

  • Sensors
  • Healthcare applications
  • Wearables
  • IoT for health
  • Signal processing
  • Decision support
  • Machine learning

Published Papers (4 papers)

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Research

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19 pages, 1831 KiB  
Article
A Framework for Malicious Traffic Detection in IoT Healthcare Environment
by Faisal Hussain, Syed Ghazanfar Abbas, Ghalib A. Shah, Ivan Miguel Pires, Ubaid U. Fayyaz, Farrukh Shahzad, Nuno M. Garcia and Eftim Zdravevski
Sensors 2021, 21(9), 3025; https://0-doi-org.brum.beds.ac.uk/10.3390/s21093025 - 26 Apr 2021
Cited by 86 | Viewed by 10720
Abstract
The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept [...] Read more.
The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment. Full article
(This article belongs to the Special Issue Wearables and IoT Sensors for Applications in Healthcare)
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Review

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38 pages, 961 KiB  
Review
Unobtrusive Sensors for the Assessment of Older Adult’s Frailty: A Scoping Review
by Antonio Cobo, Elena Villalba-Mora, Rodrigo Pérez-Rodríguez, Xavier Ferre and Leocadio Rodríguez-Mañas
Sensors 2021, 21(9), 2983; https://0-doi-org.brum.beds.ac.uk/10.3390/s21092983 - 23 Apr 2021
Cited by 4 | Viewed by 2715
Abstract
Ubiquity (devices becoming part of the context) and transparency (devices not interfering with daily activities) are very significant in healthcare monitoring applications for elders. The present study undertakes a scoping review to map the literature on sensor-based unobtrusive monitoring of older adults’ frailty. [...] Read more.
Ubiquity (devices becoming part of the context) and transparency (devices not interfering with daily activities) are very significant in healthcare monitoring applications for elders. The present study undertakes a scoping review to map the literature on sensor-based unobtrusive monitoring of older adults’ frailty. We aim to determine what types of devices comply with unobtrusiveness requirements, which frailty markers have been unobtrusively assessed, which unsupervised devices have been tested, the relationships between sensor outcomes and frailty markers, and which devices can assess multiple markers. SCOPUS, PUBMED, and Web of Science were used to identify papers published 2010–2020. We selected 67 documents involving non-hospitalized older adults (65+ y.o.) and assessing frailty level or some specific frailty-marker with some sensor. Among the nine types of body worn sensors, only inertial measurement units (IMUs) on the waist and wrist-worn sensors comply with ubiquity. The former can transparently assess all variables but weight loss. Wrist-worn devices have not been tested in unsupervised conditions. Unsupervised presence detectors can predict frailty, slowness, performance, and physical activity. Waist IMUs and presence detectors are the most promising candidates for unobtrusive and unsupervised monitoring of frailty. Further research is necessary to give specific predictions of frailty level with unsupervised waist IMUs. Full article
(This article belongs to the Special Issue Wearables and IoT Sensors for Applications in Healthcare)
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23 pages, 542 KiB  
Review
Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations
by Yiyuan Zhang, Ine D’Haeseleer, José Coelho, Vero Vanden Abeele and Bart Vanrumste
Sensors 2021, 21(6), 2176; https://0-doi-org.brum.beds.ac.uk/10.3390/s21062176 - 20 Mar 2021
Cited by 7 | Viewed by 3525
Abstract
This article provides a systematic review of studies on recognising bathroom activities in older adults using wearable sensors. Bathroom activities are an important part of Activities of Daily Living (ADL). The performance on ADL activities is used to predict the ability of older [...] Read more.
This article provides a systematic review of studies on recognising bathroom activities in older adults using wearable sensors. Bathroom activities are an important part of Activities of Daily Living (ADL). The performance on ADL activities is used to predict the ability of older adults to live independently. This paper aims to provide an overview of the studied bathroom activities, the wearable sensors used, different applied methodologies and the tested activity recognition techniques. Six databases were screened up to March 2020, based on four categories of keywords: older adults, activity recognition, bathroom activities and wearable sensors. In total, 4262 unique papers were found, of which only seven met the inclusion criteria. This small number shows that few studies have been conducted in this field. Therefore, in addition, this critical review resulted in several recommendations for future studies. In particular, we recommend to (1) study complex bathroom activities, including multiple movements; (2) recruit participants, especially the target population; (3) conduct both lab and real-life experiments; (4) investigate the optimal number and positions of wearable sensors; (5) choose a suitable annotation method; (6) investigate deep learning models; (7) evaluate the generality of classifiers; and (8) investigate both detection and quality performance of an activity. Full article
(This article belongs to the Special Issue Wearables and IoT Sensors for Applications in Healthcare)
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Other

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25 pages, 5902 KiB  
Systematic Review
Wearable Devices for Biofeedback Rehabilitation: A Systematic Review and Meta-Analysis to Design Application Rules and Estimate the Effectiveness on Balance and Gait Outcomes in Neurological Diseases
by Thomas Bowman, Elisa Gervasoni, Chiara Arienti, Stefano Giuseppe Lazzarini, Stefano Negrini, Simona Crea, Davide Cattaneo and Maria Chiara Carrozza
Sensors 2021, 21(10), 3444; https://0-doi-org.brum.beds.ac.uk/10.3390/s21103444 - 15 May 2021
Cited by 38 | Viewed by 6807
Abstract
Wearable devices are used in rehabilitation to provide biofeedback about biomechanical or physiological body parameters to improve outcomes in people with neurological diseases. This is a promising approach that influences motor learning and patients’ engagement. Nevertheless, it is not yet clear what the [...] Read more.
Wearable devices are used in rehabilitation to provide biofeedback about biomechanical or physiological body parameters to improve outcomes in people with neurological diseases. This is a promising approach that influences motor learning and patients’ engagement. Nevertheless, it is not yet clear what the most commonly used sensor configurations are, and it is also not clear which biofeedback components are used for which pathology. To explore these aspects and estimate the effectiveness of wearable device biofeedback rehabilitation on balance and gait, we conducted a systematic review by electronic search on MEDLINE, PubMed, Web of Science, PEDro, and the Cochrane CENTRAL from inception to January 2020. Nineteen randomized controlled trials were included (Parkinson’s n = 6; stroke n = 13; mild cognitive impairment n = 1). Wearable devices mostly provided real-time biofeedback during exercise, using biomechanical sensors and a positive reinforcement feedback strategy through auditory or visual modes. Some notable points that could be improved were identified in the included studies; these were helpful in providing practical design rules to maximize the prospective of wearable device biofeedback rehabilitation. Due to the current quality of the literature, it was not possible to achieve firm conclusions about the effectiveness of wearable device biofeedback rehabilitation. However, wearable device biofeedback rehabilitation seems to provide positive effects on dynamic balance and gait for PwND, but higher-quality RCTs with larger sample sizes are needed for stronger conclusions. Full article
(This article belongs to the Special Issue Wearables and IoT Sensors for Applications in Healthcare)
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