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Smart IoT Systems for Pervasive Healthcare

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

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 53131

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Assoc. Prof. Dr. Nauman Aslam

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Department of Computer and Information Science, Faculty of Engineering & Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, UK
Interests: Internet of Things (IoT); energy-efficient communication protocols; medical applications of wireless sensor networks; security and privacy-enhancing technologies
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Dr. Qasim Ahmed

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Department of Engineering and Technology, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
Interests: signal processing; electrical engineering; telecommunications engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the tremendous growth in the Internet of Things (IoT) in recent years, it is expected that smart IoT systems will play a vital role in many pervasive healthcare applications. IoT integrated with other technologies could significantly transform the landscape of pervasive care through uninterrupted and ubiquitous monitoring and rehabilitation of patients in private and public care. While the IoT has extended the concept of traditional wireless sensor networks (WSNs) and wireless body sensor networks (WBANs), smart IoTs in pervasive medical care application takes this even further by harnessing the power of machine learning, data analytics, cloud computing, and decision support systems. Many applications of smart IoT systems such as fall detection, patient surveillance, early detection of medical diseases/conditions, detection of smart drug delivery, etc. will greatly benefit the future healthcare systems. There are a number of research challenges to fully realizing the advantages of smart IoT systems, including heterogeneity and scalability issues, quality of service for medical data, privacy, security, and trust issues, and further energy optimization issues and problems that arise from dealing with massive datasets. This Special Issue encourages submissions of high-quality unpublished papers in both theoretical and experimental research. Topics of interest include but are not limited to the following:

Architecture and protocols for an IoT-based healthcare system
Artificial intelligence and machine learning for IoT systems in pervasive healthcare
Control and decision making for smart IoT systems
Integration of IoT systems with fog and edge computing
Security and privacy models for healthcare data in IoT systems
Interoperability of heterogeneous technologies for IoT systems in pervasive healthcare
QoS-aware communication protocols for IoT systems in pervasive healthcare
Location-aware protocols and localization in IoT-based healthcare system
Human-machine interface (HMI) technology for pervasive healthcare
Smart sensors and wearable devices for wireless body area networks (WBANs)
Energy-aware protocols and standards in IoT-enabled healthcare
Data analytics and predictive models for IoT systems in pervasive healthcare
Case studies and applications in IoT for pervasive healthcare

Assoc. Prof. Dr. Nauman Aslam
Dr. Qasim Ahmed
Guest Editors

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Keywords

Smart Internet of Things (IoT)
Pervasive healthcare
Intelligent systems
Communication technologies and protocols
AI and machine learning

Published Papers (9 papers)

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Research

Jump to: Review

26 pages, 4558 KiB

Open AccessArticle

Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia

by Farhad Ahamed, Seyed Shahrestani and Hon Cheung

Sensors 2020, 20(21), 6031; https://doi.org/10.3390/s20216031 - 23 Oct 2020

Cited by 27 | Viewed by 3933

Abstract

Identifying the symptoms of the early stages of dementia is a difficult task, particularly for older adults living in residential care. Internet of Things (IoT) and smart environments can assist with the early detection of dementia, by nonintrusive monitoring of the daily activities of the older adults. In this work, we focus on the daily life activities of adults in a smart home setting to discover their potential cognitive anomalies using a public dataset. After analysing the dataset, extracting the features, and selecting distinctive features based on dynamic ranking, a classification model is built. We compare and contrast several machine learning approaches for developing a reliable and efficient model to identify the cognitive status of monitored adults. Using our predictive model and our approach of distinctive feature selection, we have achieved 90.74% accuracy in detecting the onset of dementia. Full article

(This article belongs to the Special Issue Smart IoT Systems for Pervasive Healthcare)

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20 pages, 3512 KiB

Open AccessArticle

Nonintrusive Fine-Grained Home Care Monitoring: Characterizing Quality of In-Home Postural Changes Using Bone-Based Human Sensing

by Sinan Chen, Sachio Saiki and Masahide Nakamura

Sensors 2020, 20(20), 5894; https://doi.org/10.3390/s20205894 - 18 Oct 2020

Cited by 10 | Viewed by 2617

Abstract

In contrast to the physical activities of able-bodied people at home, most people who require long-term specific care (e.g., bedridden patients and patients who have difficulty walking) usually show more low-intensity slow physical activities with postural changes. Although the existing devices can detect data such as heart rate and the number of steps, they have been increasing the physical burden relying on long-term wearing. The purpose of this paper is to realize a noninvasive fine-grained home care monitoring system that is sustainable for people requiring special care. In the proposed method, we present a novel technique that integrates inexpensive camera devices and bone-based human sensing technologies to characterize the quality of in-home postural changes. We realize a local process in feature data acquisition once per second, which extends from a computer browser to Raspberry Pi. Our key idea is to regard the changes of the bounding box output by standalone pose estimation models in the shape and distance as the quality of the pose conversion, body movement, and positional changes. Furthermore, we use multiple servers to realize distributed processing that uploads data to implement home monitoring as a web service. Based on the experimental results, we conveyed our findings and advice to the subject that include where the daily living habits and the irregularity of home care timings needed improvement. Full article

(This article belongs to the Special Issue Smart IoT Systems for Pervasive Healthcare)

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19 pages, 3157 KiB

Open AccessArticle

A COVID-19-Based Modified Epidemiological Model and Technological Approaches to Help Vulnerable Individuals Emerge from the Lockdown in the UK

by Dario Ortega Anderez, Eiman Kanjo, Ganna Pogrebna, Omprakash Kaiwartya, Shane D. Johnson and John Alan Hunt

Sensors 2020, 20(17), 4967; https://doi.org/10.3390/s20174967 - 02 Sep 2020

Cited by 19 | Viewed by 5705

Abstract

COVID-19 has shown a relatively low case fatality rate in young healthy individuals, with the majority of this group being asymptomatic or having mild symptoms. However, the severity of the disease among the elderly as well as in individuals with underlying health conditions has caused significant mortality rates worldwide. Understanding this variance amongst different sectors of society and modelling this will enable the different levels of risk to be determined to enable strategies to be applied to different groups. Long-established compartmental epidemiological models like SIR and SEIR do not account for the variability encountered in the severity of the SARS-CoV-2 disease across different population groups. The objective of this study is to investigate how a reduction in the exposure of vulnerable individuals to COVID-19 can minimise the number of deaths caused by the disease, using the UK as a case study. To overcome the limitation of long-established compartmental epidemiological models, it is proposed that a modified model, namely SEIR-v, through which the population is separated into two groups regarding their vulnerability to SARS-CoV-2 is applied. This enables the analysis of the spread of the epidemic when different contention measures are applied to different groups in society regarding their vulnerability to the disease. A Monte Carlo simulation (100,000 runs) along the proposed SEIR-v model is used to study the number of deaths which could be avoided as a function of the decrease in the exposure of vulnerable individuals to the disease. The results indicate a large number of deaths could be avoided by a slight realistic decrease in the exposure of vulnerable groups to the disease. The mean values across the simulations indicate 3681 and 7460 lives could be saved when such exposure is reduced by 10% and 20% respectively. From the encouraging results of the modelling a number of mechanisms are proposed to limit the exposure of vulnerable individuals to the disease. One option could be the provision of a wristband to vulnerable people and those without a smartphone and contact-tracing app, filling the gap created by systems relying on smartphone apps only. By combining very dense contact tracing data from smartphone apps and wristband signals with information about infection status and symptoms, vulnerable people can be protected and kept safer. Full article

(This article belongs to the Special Issue Smart IoT Systems for Pervasive Healthcare)

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20 pages, 1739 KiB

Open AccessArticle

Knowledge-Based Decision Support in Healthcare via Near Field Communication

by Giuseppe Loseto, Floriano Scioscia, Michele Ruta, Filippo Gramegna, Saverio Ieva, Agnese Pinto and Crescenzio Scioscia

Sensors 2020, 20(17), 4923; https://doi.org/10.3390/s20174923 - 31 Aug 2020

Cited by 5 | Viewed by 2437

Abstract

The benefits of automatic identification technologies in healthcare have been largely recognized. Nevertheless, unlocking their potential to support the most knowledge-intensive medical tasks requires to go beyond mere item identification. This paper presents an innovative Decision Support System (DSS), based on a semantic enhancement of Near Field Communication (NFC) standard. Annotated descriptions of medications and patient’s case history are stored in NFC transponders and used to help caregivers providing the right therapy. The proposed framework includes a lightweight reasoning engine to infer possible incompatibilities in treatment, suggesting substitute therapies. A working prototype is presented in a rheumatology case study and preliminary performance tests are reported. The approach is independent from back-end infrastructures. The proposed DSS framework is validated in a limited but realistic case study, and performance evaluation of the prototype supports its practical feasibility. Automated reasoning on knowledge fragments extracted via NFC enables effective decision support not only in hospital centers, but also in pervasive IoT-based healthcare contexts such as first aid, ambulance transport, rehabilitation facilities and home care. Full article

(This article belongs to the Special Issue Smart IoT Systems for Pervasive Healthcare)

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30 pages, 1365 KiB

Open AccessArticle

Emerging Wireless Sensor Networks and Internet of Things Technologies—Foundations of Smart Healthcare

by Gordana Gardašević, Konstantinos Katzis, Dragana Bajić and Lazar Berbakov

Sensors 2020, 20(13), 3619; https://doi.org/10.3390/s20133619 - 27 Jun 2020

Cited by 45 | Viewed by 8044

Abstract

Future smart healthcare systems—often referred to as Internet of Medical Things (IoMT) – will combine a plethora of wireless devices and applications that use wireless communication technologies to enable the exchange of healthcare data. Smart healthcare requires sufficient bandwidth, reliable and secure communication links, energy-efficient operations, and Quality of Service (QoS) support. The integration of Internet of Things (IoT) solutions into healthcare systems can significantly increase intelligence, flexibility, and interoperability. This work provides an extensive survey on emerging IoT communication standards and technologies suitable for smart healthcare applications. A particular emphasis has been given to low-power wireless technologies as a key enabler for energy-efficient IoT-based healthcare systems. Major challenges in privacy and security are also discussed. A particular attention is devoted to crowdsourcing/crowdsensing, envisaged as tools for the rapid collection of massive quantities of medical data. Finally, open research challenges and future perspectives of IoMT are presented. Full article

(This article belongs to the Special Issue Smart IoT Systems for Pervasive Healthcare)

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16 pages, 3396 KiB

Open AccessArticle

Prediction of Body Weight of a Person Lying on a Smart Mat in Nonrestraint and Unconsciousness Conditions

by Tae-Hwan Kim and Youn-Sik Hong

Sensors 2020, 20(12), 3485; https://doi.org/10.3390/s20123485 - 19 Jun 2020

Cited by 9 | Viewed by 3686

Abstract

We want to predict body weight while lying in bed for an elderly patient who is unable to move by himself/herself. To this end, we have implemented a prototype system that estimates the body weight of a person lying on a smart mat in nonrestraint and unconsciousness conditions. A total of 128 FSR (force sensing resistor) sensors were placed in a 16 × 8-grid structure on the smart mat. We formulated three methods based on the features to be applied: segmentation, average cumulative sum of pressure, and serialization. All the proposed methods were implemented with four different machine-learning models: regression, deep neural network (DNN), convolutional neural network (CNN), and random forest. We compared their performance using MAE and RMSE as evaluation criteria. From the experimental results, we chose the serialization method with the DNN model as the best model. Despite the limitations of the presence of dead space due to the wide spacing between the sensors and the small dataset, the MAE and the RMSE of the body weight prediction of the proposed method was 4.608 and 5.796, respectively. That is, it showed an average error of ±4.6 kg for the average weight of 72.9 kg. Full article

(This article belongs to the Special Issue Smart IoT Systems for Pervasive Healthcare)

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19 pages, 7687 KiB

Open AccessArticle

A Robust Quasi-Quantum Walks-based Steganography Protocol for Secure Transmission of Images on Cloud-based E-healthcare Platforms

by Bassem Abd-El-Atty, Abdullah M. Iliyasu, Haya Alaskar and Ahmed A. Abd El-Latif

Sensors 2020, 20(11), 3108; https://doi.org/10.3390/s20113108 - 31 May 2020

Cited by 72 | Viewed by 4169

Abstract

Traditionally, tamper-proof steganography involves using efficient protocols to encrypt the stego cover image and/or hidden message prior to embedding it into the carrier object. However, as the inevitable transition to the quantum computing paradigm beckons, its immense computing power will be exploited to violate even the best non-quantum, i.e., classical, stego protocol. On its part, quantum walks can be tailored to utilise their astounding ‘quantumness’ to propagate nonlinear chaotic behaviours as well as its sufficient sensitivity to alterations in primary key parameters both important properties for efficient information security. Our study explores using a classical (i.e., quantum-inspired) rendition of the controlled alternate quantum walks (i.e., CAQWs) model to fabricate a robust image steganography protocol for cloud-based E-healthcare platforms by locating content that overlays the secret (or hidden) bits. The design employed in our technique precludes the need for pre and/or post encryption of the carrier and secret images. Furthermore, our design simplifies the process to extract the confidential (hidden) information since only the stego image and primary states to run the CAQWs are required. We validate our proposed protocol on a dataset of medical images, which exhibited remarkable outcomes in terms of their security, good visual quality, high resistance to data loss attacks, high embedding capacity, etc., making the proposed scheme a veritable strategy for efficient medical image steganography. Full article

(This article belongs to the Special Issue Smart IoT Systems for Pervasive Healthcare)

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32 pages, 4782 KiB

Open AccessArticle

A Multiobjective, Lion Mating Optimization Inspired Routing Protocol for Wireless Body Area Sensor Network Based Healthcare Applications

by Muhammad Faheem, Rizwan Aslam Butt, Basit Raza, Hani Alquhayz, Muhammad Zahid Abbas, Md Asri Ngadi and Vehbi Cagri Gungor

Sensors 2019, 19(23), 5072; https://doi.org/10.3390/s19235072 - 20 Nov 2019

Cited by 11 | Viewed by 3143

Abstract

The importance of body area sensor networks (BASNs) is increasing day by day because of their increasing use in Internet of things (IoT)-enabled healthcare application services. They help humans in improving their quality of life by continuously monitoring various vital signs through biosensors strategically placed on the human body. However, BASNs face serious challenges, in terms of the short life span of their batteries and unreliable data transmission, because of the highly unstable and unpredictable channel conditions of tiny biosensors located on the human body. These factors may result in poor data gathering quality in BASNs. Therefore, a more reliable data transmission mechanism is greatly needed in order to gather quality data in BASN-based healthcare applications. Therefore, this study proposes a novel, multiobjective, lion mating optimization inspired routing protocol, called self-organizing multiobjective routing protocol (SARP), for BASN-based IoT healthcare applications. The proposed routing scheme significantly reduces local search problems and finds the best dynamic cluster-based routing solutions between the source and destination in BASNs. Thus, it significantly improves the overall packet delivery rate, residual energy, and throughput with reduced latency and packet error rates in BASNs. Extensive simulation results validate the performance of our proposed SARP scheme against the existing routing protocols in terms of the packet delivery ratio, latency, packet error rate, throughput, and energy efficiency for BASN-based health monitoring applications. Full article

(This article belongs to the Special Issue Smart IoT Systems for Pervasive Healthcare)

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Review

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58 pages, 21886 KiB

Open AccessEditor’s ChoiceReview

Wireless Power Transfer Techniques for Implantable Medical Devices: A Review

by Sadeque Reza Khan, Sumanth Kumar Pavuluri, Gerard Cummins and Marc P. Y. Desmulliez

Sensors 2020, 20(12), 3487; https://doi.org/10.3390/s20123487 - 19 Jun 2020

Cited by 148 | Viewed by 18286

Abstract

Wireless power transfer (WPT) systems have become increasingly suitable solutions for the electrical powering of advanced multifunctional micro-electronic devices such as those found in current biomedical implants. The design and implementation of high power transfer efficiency WPT systems are, however, challenging. The size of the WPT system, the separation distance between the outside environment and location of the implanted medical device inside the body, the operating frequency and tissue safety due to power dissipation are key parameters to consider in the design of WPT systems. This article provides a systematic review of the wide range of WPT systems that have been investigated over the last two decades to improve overall system performance. The various strategies implemented to transfer wireless power in implantable medical devices (IMDs) were reviewed, which includes capacitive coupling, inductive coupling, magnetic resonance coupling and, more recently, acoustic and optical powering methods. The strengths and limitations of all these techniques are benchmarked against each other and particular emphasis is placed on comparing the implanted receiver size, the WPT distance, power transfer efficiency and tissue safety presented by the resulting systems. Necessary improvements and trends of each WPT techniques are also indicated per specific IMD. Full article

(This article belongs to the Special Issue Smart IoT Systems for Pervasive Healthcare)

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