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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: 16 December 2022 | Viewed by 7873
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 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 2400 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 (7 papers)

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Research

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Article
Simultaneous Gut-Brain Electrophysiology Shows Cognition and Satiety Specific Coupling
Sensors 2022, 22(23), 9242; https://doi.org/10.3390/s22239242 - 28 Nov 2022
Viewed by 92
Abstract
Recent studies, using high resolution magnetoencephalography (MEG) and electrogastrography (EGG), have shown that during resting state, rhythmic gastric physiological signals are linked with cortical brain oscillations. Yet, gut-brain coupling has not been investigated with electroencephalography (EEG) during cognitive brain engagement or during hunger-related [...] Read more.
Recent studies, using high resolution magnetoencephalography (MEG) and electrogastrography (EGG), have shown that during resting state, rhythmic gastric physiological signals are linked with cortical brain oscillations. Yet, gut-brain coupling has not been investigated with electroencephalography (EEG) during cognitive brain engagement or during hunger-related gut engagement. In this study in 14 young adults (7 females, mean ± SD age 25.71 ± 8.32 years), we study gut-brain coupling using simultaneous EEG and EGG during hunger and satiety states measured in separate visits, and compare responses both while resting as well as during a cognitively demanding working memory task. We find that EGG-EEG phase-amplitude coupling (PAC) differs based on both satiety state and cognitive effort, with greater PAC modulation observed in the resting state relative to working memory. We find a significant interaction between gut satiation levels and cognitive states in the left fronto-central brain region, with larger cognitive demand based differences in the hunger state. Furthermore, strength of PAC correlated with behavioral performance during the working memory task. Altogether, these results highlight the role of gut-brain interactions in cognition and demonstrate the feasibility of these recordings using scalable sensors. Full article
(This article belongs to the Special Issue Sensors and IoT in Modern Healthcare Delivery and Applications)
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Article
A Proof-of-Concept IoT System for Remote Healthcare Based on Interoperability Standards
Sensors 2022, 22(4), 1646; https://doi.org/10.3390/s22041646 - 19 Feb 2022
Cited by 1 | Viewed by 1033
Abstract
The Internet of Things paradigm in healthcare has boosted the design of new solutions for the promotion of healthy lifestyles and the remote care. Thanks to the effort of academia and industry, there is a wide variety of platforms, systems and commercial products [...] Read more.
The Internet of Things paradigm in healthcare has boosted the design of new solutions for the promotion of healthy lifestyles and the remote care. Thanks to the effort of academia and industry, there is a wide variety of platforms, systems and commercial products enabling the real-time information exchange of environmental data and people’s health status. However, one of the problems of these type of prototypes and solutions is the lack of interoperability and the compromised scalability in large scenarios, which limits its potential to be deployed in real cases of application. In this paper, we propose a health monitoring system based on the integration of rapid prototyping hardware and interoperable software to build system capable of transmitting biomedical data to healthcare professionals. The proposed system involves Internet of Things technologies and interoperablility standards for health information exchange such as the Fast Healthcare Interoperability Resources and a reference framework architecture for Ambient Assisted Living UniversAAL. Full article
(This article belongs to the Special Issue Sensors and IoT in Modern Healthcare Delivery and Applications)
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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
Cited by 1 | Viewed by 925
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
Cited by 12 | Viewed by 1305
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
Cited by 3 | Viewed by 859
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
Cited by 3 | Viewed by 1174
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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Review

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Review
Technological Solutions for Social Isolation Monitoring of the Elderly: A Survey of Selected Projects from Academia and Industry
Sensors 2022, 22(22), 8802; https://doi.org/10.3390/s22228802 - 14 Nov 2022
Viewed by 298
Abstract
Social isolation is likely to be one of the most serious health outcomes for the elderly due to the COVID-19 pandemic, especially for seniors living alone at home. In fact, two approaches have been used to assess social isolation. The first is a [...] Read more.
Social isolation is likely to be one of the most serious health outcomes for the elderly due to the COVID-19 pandemic, especially for seniors living alone at home. In fact, two approaches have been used to assess social isolation. The first is a self-reported survey designed for research purposes. The second approach is the use of monitoring technology. The objective of this paper is to provide some illustrative publications, works and examples of the current status and future prospects in the field of monitoring systems that focused on two main activities of daily living: meal-taking activity (shopping, cooking, eating and washing dishes) and mobility (inside the home and the act of going out). These two activities combined seem relevant to a potential risk of social isolation in the elderly. Although current research focuses on identifying only ADLs, we propose to use them as a first step to extract daily habits and risk level of social isolation. Moreover, since activity recognition is a recent field, we raise specific problems as well as needed contributions and we propose directions and research opportunities to accelerate advances in this field. Full article
(This article belongs to the Special Issue Sensors and IoT in Modern Healthcare Delivery and Applications)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Evaluation of Orthotic Treatment Methods for Patients with Diabetic Foot Ulcers
Authors: Dana Elfarra; Jikhil Jikki; Allen Biju; Bisola Mutingwende; George Oguntala
Affiliation: Biomedical Engineering Department of Life Sciences, School of Health Sciences Faculty of Health, Education and Life Sciences Birmingham City University, West Midland, UK
Abstract: Diabetic foot ulcers are prevalent health complications of diabetic foot disease. Such complications often lead to morbidity, which results in significant economic burden on patients and society. To ameliorate such heath and economic burden, it is critical to understand the efficacy of therapeutic methods for diabetic foot ulcers. In this paper, the efficacy of orthotic treatment methods for diabetic foot ulcers is evaluated. The Codamotion system is used to measure the ankle and knee angles and the BioWare force plate system to establish the ground reaction forces during Gait. The obtained result of the analysed data shows that the external EVA rocker-sole without a PORON grey insole is the optimal treatment method. However, the optimal treatment method becomes ineffective for patients with reduced balance or unstable posture. Further, the addition of a PORON grey insole reduces space within the shoe leading to increased plantar pressure, which invariably slows down the healing process of diabetic foot ulcers.

Title: Development of Testing Protocol to improve Venous Needle Dislodgement Detection
Authors: Allen Biju; Dana Elfarra; Jikhil Jikki; Vijayalakshmi Ahanathapillai; George Oguntala
Affiliation: Department of Biomedical and Electronic Engineering, Faculty of Engineering and InformaticsUniversity of Bradford1, Richmond Road, Bradford, West Yorkshire, UK
Abstract: Inadvertent venous needle dislodgement (VND) from graft or arteriovenous fistula can lead from non-fatal to serious life-threatening complications during haemodialysis treatment. In the occurrence of VND, if the blood pump is not stopped, either manually or via activation of the protective system of the dialysis machine, an adult patient can bleed to death within a few minutes. In this paper, the design of a new testing protocol to improve VND detection is presented. The proof-of-concept VND detector, developed by Quanta Dialysis is employed. The VND detector is subjected to four (4) testing protocols to evaluate the effectiveness of the VND detector within a 12-seconds window. Investigation on different clinical scenarios to simulate VND is conducted to support the development of a series of testing protocols. The quantitative and qualitative data generated via the testing protocols were used to critically analyse various VND detection devices and evaluated using statistical analysis. The obtained results from the proof-of-concept VND detector detect VND within 12 seconds.

Title: Design of a Novel in-ear Sleep Detection Device
Authors: Jikhil Jikki; Allen Biju; Dana Elfarra; George Oguntala; Vivek Indramohan
Affiliation: Biomedical Engineering Department of Life Sciences, School of Health Sciences Faculty of Health, Education and Life Sciences Birmingham City University, West Midland, UK
Abstract: Future healthcare applications tend towards real-time remote monitoring of patient health conditions. Sleep is critical to healthy living and lack of adequate sleep is associated with various health conditions. One common cause is obstructive sleep apnoea with low awareness. To this end, the monitoring of sleep patterns without the constant involvement of medical specialists has become an interesting clinical question. In this study, an in-ear sleep detection device that incorporates a PPG sensor to collect heart rate data at a given threshold, indicative of sleep pattern, is developed and investigated. The overall solution includes the PPG-sensor device that connects via Bluetooth to a customised Arduino-based app that visualises the sleep pattern on an Android device. Further, the developed device in comparison with a UK hospital-issued finger pulse oximeter, show that the PPG sensor has a percentage error of less than 5%, with the largest deviation of +3 BPM.

Title: Enhancing mHealth Data Collection Applications with Sensing Capabilities
Authors: Maximilian Karthan; Robin Martin; Felix Holl; Walter Swoboda; Hans A. Kestler; Rüdiger Pryss; Johannes Schobel
Affiliation: Neu-Ulm University of Applied Sciences, Germany
Abstract: Smart mobile devices like smartphones or tablets have become a salient factor for collecting data in complex healthcare scenarios (i.e., psychological studies, medical trials), slowly replacing traditional pen-and-paper instruments. However, by simply digitizing such instruments, the full potential of such devices is not fully utilized yet: Most modern devices comprise a variety of different sensing capabilities (i.e., microphone, GPS data, camera, ...), which may also provide valuable data and potentially valuable insights for healthcare professionals and researchers. Considerable development effort is required to integrate sensing capabilities into (existing) data collection applications. Developers may have to deal with platform-specific peculiarities (i.e., Android vs. iOS) or proprietary sensor data formats, which lead to unnecessary development overhead when supporting researchers with such digital solutions. A cross-platform mobile sensing framework has been developed to enhance existing data collection applications with sensing capabilities to cope with these issues. This framework will allow researchers to collect additional information from participants as well as their environment, increasing the amount of data that is collected and potential new insights from existing data.

Title: Technological solutions for social isolation monitoring of the elderly: A systematic review
Authors: Ghazi Bouaziz; Damien Brulin; Eric Campo
Affiliation: LAAS-CNRS, Université de Toulouse, CNRS, UT2J, Toulouse, France
Abstract: Social isolation is likely to be one of the most serious health outcomes for the elderly due to the COVID-19 pandemic, especially for seniors living alone at home. In fact, two approaches have been used to assess social isolation. The first is a self-reported survey designed for research purposes. The second approach is the use of monitoring technology. The objective of this paper is to provide some illustrative publications, works and examples of the current status and future prospects in the field of monitoring systems that focused on two main activities of daily living: meal-taking activity (shopping, cooking, eating and washing dishes) and mobility (inside the home and the act of going out). These two activities combined seem relevant for a prediction of social isolation. Finally, we present our solution that extract ADLs related to mobility and eating, adult’s daily pattern and the risk of social isolation.

Title: Simultaneous gut-brain electrophysiology shows cognition and satiety specific coupling
Authors: Pragathi Priyadharsini Balasubramani; Anuja Walke; Gillian Grennan; Andrew Perley; Suzanna Purpura; Dhakshin Ramanathan; Todd Coleman; Jyoti Mishra
Affiliation: University of California San Diego Medical Center, USA
Abstract: Recent studies, using high resolution magnetoencephalography (MEG) and electrogastrography (EGG), have shown that during resting state, rhythmic gastric physiological signals are linked with cortical brain oscillations. Yet, gut-brain coupling has not been investigated with electroencephalography (EEG) during cognitive brain engagement or during hunger-related gut engagement. In this study in 14 young adults (7 females, mean±SD age 25.71±8.32 years), we study gut-brain coupling using simultaneous EEG and EGG during hunger and satiety states measured in separate visits, and compare responses both while resting as well as during a cognitively demanding working memory task. We find that EGG-EEG phase-amplitude coupling differs based on both satiety state and cognitive effort. Furthermore, strength of phase-amplitude coupling correlates with behavioral performance during the working memory task. Altogether, these results highlight the important role of gut-brain interactions in cognition using scalable sensors.

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