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Special Issue "Sensors and IoT in Modern Healthcare Delivery and Applications"

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

Deadline for manuscript submissions: closed (30 November 2021).
Please contact the Guest Editor or the Section Managing Editor at ([email protected]) for any queries.

Special Issue Editors

Dr. George Oguntala
E-Mail Website
Guest Editor
Biomedical Engineering Department of Life Sciences, School of Health Sciences Faculty of Health, Education and Life Sciences Birmingham City University, West Midland, UK
Interests: Activity Recognition; Digital Health; IoT; Smart Home and Smart City; RFID systems; Applied and Computational Mathematics in Biology
Prof. Dr. Raed A. Abd-Alhameed
E-Mail Website
Guest Editor
Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UK
Interests: energy-efficient front-end design; radio frequency; energy harvesting; communications systems; 5G communications; sensor design; localisation-based services; signal processing; optimisation process; MIMO system design; health hazards; propagations, antennas and electromagnetic computational techniques
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yim-Fun Hu
E-Mail Website
Guest Editor
Department of Biomedical and Electronics Engineering, University of Bradford, Bradford BD7 1DP, UK
Interests: mobile, wireless and satellite communications; integrated communications networks—protocols, resource, mobility and network management; cyber security; software-defined networks; network virtualisation; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rising numbers of chronic disease incidence and global population density, the demand for cost-effective healthcare is unprecedented. However, with the evolving health conditions, traditional healthcare systems become ineffective to satisfy the continuously growing needs of our developing society. The Internet of Things (IoT) and sensor networks are emerging technologies of multidisciplinary approach and exceptional popularity in healthcare. Sensor networks comprise resource-constrained devices to gather data from the environment, allowing the continuous and real-time collection of an individual’s health information and their related behaviour. The IoT serves as a bridging platform that connects the physical world and cyberspace so that healthcare services and applications are delivered with high efficiency and productivity. The evolving ecosystem of IoT and sensor networks as a technological paradigm in healthcare have revolutionised different aspects of traditional healthcare and ambient assisted living paradigms. The integration of IoT devices with medical applications improves their performance, resulting in high-quality service to patients. IoT and sensor networks are prevalent in connected health or technology-enabled care and its variants, including telehealth, mHealth and eHealth, and thus improve healthcare delivery and diagnostic tools. Nonetheless, further research efforts on IoT systems, sensor networks and architectures for large-scale deployments is imperative to bridge the gaps between cost-effective implementations and seamless service requirements. This Special Issue aims at addressing topics on new contributions to the state-of-the-art in various domains of IoT and sensor networks, ranging from architectural models to specific implementation approaches. Additionally, focus will be given to areas related to data analytics and machine learning and deep learning in modelling and deploying secure and trustworthy energy-aware solutions in IoT. Further, advances and challenges related to deploying sensor and wireless network for IoT-based healthcare solutions are desirable.

Dr. George Oguntala
Prof. Yim-Fun Hu
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

  • technology-enabled care architectures and applications for IoT
  • Internet of Wearable Things
  • sensor networks
  • activity recognition
  • antenna and sensor design for IoT
  • Ambient Intelligence
  • threat modelling and risk assessment in IoT
  • trust and identity management in IoT for healthcare delivery
  • interpretable machine learning and bioinformatics
  • testbed and experimental results for IoT
  • state-of-the-art IoT devices
  • physical, physiological, cognitive and behavioural processes

Published Papers (4 papers)

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Research

Article
Development of an Integrated EEG/fNIRS Brain Function Monitoring System
Sensors 2021, 21(22), 7703; https://doi.org/10.3390/s21227703 - 19 Nov 2021
Viewed by 200
Abstract
In this study, a fully integrated electroencephalogram/functional near-infrared spectroscopy (EEG/fNIRS) brain monitoring system was designed to fulfill the demand for a miniaturized, light-weight, low-power-consumption, and low-cost brain monitoring system as a potential tool with which to screen for brain diseases. The system is [...] Read more.
In this study, a fully integrated electroencephalogram/functional near-infrared spectroscopy (EEG/fNIRS) brain monitoring system was designed to fulfill the demand for a miniaturized, light-weight, low-power-consumption, and low-cost brain monitoring system as a potential tool with which to screen for brain diseases. The system is based on the ADS1298IPAG Analog Front-End (AFE) and can simultaneously acquire two-channel EEG signals with a sampling rate of 250 SPS and six-channel fNIRS signals with a sampling rate of 8 SPS. AFE is controlled by Teensy 3.2 and powered by a lithium polymer battery connected to two protection circuits and regulators. The acquired EEG and fNIRS signals are monitored and stored using a Graphical User Interface (GUI). The system was evaluated by implementing several tests to verify its ability to simultaneously acquire EEG and fNIRS signals. The implemented system can acquire EEG and fNIRS signals with a CMRR of −115 dB, power consumption of 0.75 mW/ch, system weight of 70.5 g, probe weight of 3.1 g, and a total cost of USD 130. The results proved that this system can be qualified as a low-cost, light-weight, low-power-consumption, and fully integrated EEG/fNIRS brain monitoring system. Full article
(This article belongs to the Special Issue Sensors and IoT in Modern Healthcare Delivery and Applications)
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Article
MIMO Antenna System for Modern 5G Handheld Devices with Healthcare and High Rate Delivery
Sensors 2021, 21(21), 7415; https://doi.org/10.3390/s21217415 - 08 Nov 2021
Viewed by 444
Abstract
In this work, a new prototype of the eight-element MIMO antenna system for 5G communications, internet of things, and networks has been proposed. This system is based on an H-shaped monopole antenna system that offers 200 MHz bandwidth ranges between 3.4–3.6 GHz, and [...] Read more.
In this work, a new prototype of the eight-element MIMO antenna system for 5G communications, internet of things, and networks has been proposed. This system is based on an H-shaped monopole antenna system that offers 200 MHz bandwidth ranges between 3.4–3.6 GHz, and the isolation between any two elements is well below −12 dB without using any decoupling structure. The proposed system is designed on a commercially available 0.8 mm-thick FR4 substrate. One side of the chassis is used to place the radiating elements, while the copper from the other side is being removed to avoid short-circuiting with other components and devices. This also enables space for other systems, sub-systems, and components. A prototype is fabricated and excellent agreement is observed between the experimental and the computed results. It was found that ECC is 0.2 for any two radiating elements which is consistent with the desirable standards, and channel capacity is 38 bps/Hz which is 2.9 times higher than 4 × 4 MIMO configuration. In addition, single hand mode and dual hand mode analysis are conducted to understand the operation of the system under such operations and to identify losses and/or changes in the key performance parameters. Based on the results, the proposed antenna system will find its applications in modern 5G handheld devices and internet of things with healthcare and high rate delivery. Besides that, its design simplicity will make it applicable for mass production to be used in industrial demands. Full article
(This article belongs to the Special Issue Sensors and IoT in Modern Healthcare Delivery and Applications)
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Article
Heart and Lung Sound Measurement Using an Esophageal Stethoscope with Adaptive Noise Cancellation
Sensors 2021, 21(20), 6757; https://doi.org/10.3390/s21206757 - 12 Oct 2021
Viewed by 338
Abstract
In surgeries where general anesthesia is required, the auscultation of heart and lung sounds is essential to provide information on the patient’s cardiorespiratory system. Heart and lung sounds can be recorded using an esophageal stethoscope; however, there is huge background noise when this [...] Read more.
In surgeries where general anesthesia is required, the auscultation of heart and lung sounds is essential to provide information on the patient’s cardiorespiratory system. Heart and lung sounds can be recorded using an esophageal stethoscope; however, there is huge background noise when this device is used in an operating room. In this study, a digital esophageal stethoscope system was designed. A 3D-printed case filled with Polydimethylsiloxane material was designed to hold two electret-type microphones. One of the microphones was placed inside the printed case to collect the heart and lung sound signals coming out from the patient through the esophageal catheter, the other was mounted on the surface of the case to collect the operating room sounds. A developed adaptive noise canceling algorithm was implemented to remove the operating room noise corrupted with the main heart and lung sound signals and the output signal was displayed on software application developed especially for this study. Using the designed case, the noise level of the signal was reduced to some extent, and by adding the adaptive filter, further noise reduction was achieved. The designed system is lightweight and can provide noise-free heart and lung sound signals. Full article
(This article belongs to the Special Issue Sensors and IoT in Modern Healthcare Delivery and Applications)
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Article
Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing
Sensors 2021, 21(18), 6307; https://doi.org/10.3390/s21186307 - 21 Sep 2021
Viewed by 599
Abstract
The orientation of a magneto-inertial measurement unit can be estimated using a sensor fusion algorithm (SFA). However, orientation accuracy is greatly affected by the choice of the SFA parameter values which represents one of the most critical steps. A commonly adopted approach is [...] Read more.
The orientation of a magneto-inertial measurement unit can be estimated using a sensor fusion algorithm (SFA). However, orientation accuracy is greatly affected by the choice of the SFA parameter values which represents one of the most critical steps. A commonly adopted approach is to fine-tune parameter values to minimize the difference between estimated and true orientation. However, this can only be implemented within the laboratory setting by requiring the use of a concurrent gold-standard technology. To overcome this limitation, a Rigid-Constraint Method (RCM) was proposed to estimate suboptimal parameter values without relying on any orientation reference. The RCM method effectiveness was successfully tested on a single-parameter SFA, with an average error increase with respect to the optimal of 1.5 deg. In this work, the applicability of the RCM was evaluated on 10 popular SFAs with multiple parameters under different experimental scenarios. The average residual between the optimal and suboptimal errors amounted to 0.6 deg with a maximum of 3.7 deg. These encouraging results suggest the possibility to properly tune a generic SFA on different scenarios without using any reference. The synchronized dataset also including the optical data and the SFA codes are available online. Full article
(This article belongs to the Special Issue Sensors and IoT in Modern Healthcare Delivery and Applications)
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