Internet Of Things Applications In Healthcare Research Articles

AdaBoost‐powered cloud of things framework for low‐latency, energy‐efficient chronic kidney disease prediction

AbstractThe United Nations' sustainable development agenda has set an ambitious goal of reducing premature mortality from non‐communicable diseases by 33% by 2030. Among these diseases, chronic kidney disease (CKD) is a significant contributor to both morbidity and mortality. Integrating the Internet of Things (IoT) and cloud computing in healthcare has gained momentum, particularly in remote patient monitoring. However, it is essential to acknowledge that cloud computing has limitations, particularly in handling vast volumes of Big Data, mainly due to scalability and latency concerns. This article proposes a novel framework, AdaBoostCoTCKD, to mitigate latency issues, minimize response times, reduce power consumption, and optimize network resources for predicting CKD. The framework leverages the synergy between the AdaBoost machine learning technique and fog computing paradigms to enhance the precision and efficiency of CKD prediction methods. In addition, it introduces an auxiliary cloud‐based database, enriching the pool of future insights and facilitating prospective database infrastructure expansions. This augmentation is expected to impact predictive accuracy positively. We conducted comprehensive experiments to demonstrate the effectiveness of our approach. Our model achieved an impressive training accuracy of 99.928% and testing accuracy of 99.975%, while the fog environment reduced latency by 31% and energy consumption by 75% compared to traditional cloud‐based solutions. Our proposed system enables early CKD detection and offers advantages over cloud‐only solutions, providing a robust and efficient platform for healthcare IoT applications with significant clinical value. These promising results underscore the potential of combining fog computing and the AdaBoost machine learning technique to advance healthcare by addressing latency, response time, power consumption, and network resource optimization challenges in CKD prediction.

Effective factors for the adoption of IoT applications in nursing care: A theoretical framework for smart healthcare

The utilization of the Internet of Things (IoT) presents a promising opportunity for the healthcare industry which is continuously expanding and persistently aspiring to enhance the quality of medical services and reduce its operational costs through the implementation of new technologies. Despite the proven benefits of utilizing IoT-based technologies, its integration in the healthcare sector is minimal, particularly in nursing care. The purpose of this study is to establish a conceptual model to guide program designers and implementation leaders in evaluating the adoption of IoT products and services in nursing care, from the perspective of nurses. The study employed an empirical quantitative research design utilizing a structured online questionnaire to collect data from a convenience sample of nursing staff gathered data were subjected to statistical analysis using Statistical Package for the Social Sciences (SPSS) and Structural Equation Modeling Partial Least Square (SEM-PLS). Data analysis led to the establishment of a framework to identify factors influencing the integration of IoT and barriers that may impede its adoption in nursing care. The study makes a valuable contribution to academia by addressing a gap in the existing literature relating to the adoption of IoT in medical services, particularly in nursing care, and providing a novel theoretical framework that encompasses effective factors for the integration of IoT in medical care. This model provides deep insight and guidance to healthcare industry leaders, system developers, and nursing professionals to improve program design and implementation of IoT applications in healthcare. In addition, the model guides building engineering in the planning and design of medical facilities aligned with the IoT technologies and its physical environment. Finally, the study paved the way for further research exploring the implementation of IoT-based technologies and expanding the model to other stakeholders and functions within medical departments as well as other industries.

Model‐based digital twins of medicine dispensers for healthcare IoT applications

SummaryHealthcare applications with the Internet of Things (IoT) are often safety‐critical, thus, require extensive testing. Such applications are often connected to smart medical devices from various vendors. System‐level testing of such applications requires test infrastructures physically integrating medical devices, which is time and monetary‐wise expensive. Moreover, applications continuously evolve, for example, introducing new devices and users and updating software. Nevertheless, a test infrastructure enabling testing with a few devices is insufficient for testing healthcare IoT systems, hence compromising their dependability. In this paper, we propose a model‐based approach for the creation and operation of digital twins (DTs) of medicine dispensers as a replacement for physical devices to support the automated testing of IoT applications at scale. We evaluate our approach with an industrial IoT system with medicine dispensers in the context of Oslo City and its industrial partners, providing healthcare services to its residents. We study the fidelity of DTs in terms of their functional similarities with their physical counterparts: medicine dispensers. Results show that the DTs behave more than 92% similar to the physical medicine dispensers, providing a faithful replacement for the dispenser.

AI-Driven EEC for Healthcare IoT: Security Challenges and Future Research Directions

Emerging edge computing (EEC) has been introduced as an innovative paradigm for the healthcare applications of the Internet of Things (IoT) that aims to distribute the network resources at the network edges to improve security, communication, and decision-making processes. The operation of healthcare IoT applications typically needs the presence of interoperable modules. Despite numerous benefits, these applications face many security challenges at the network edge. In this context, advanced Artificial Intelligence techniques can be used at the network edges for these applications to efficiently utilize the available resources securely. To this end, we aim to present a detailed survey of healthcare IoT applications in the context of AI-enabled EEC technology to identify unresolved security challenges that need attention from the research community and healthcare stakeholders, and then suggest potential research directions to give a clear future insight.

A Bibliometrics Analysis of Medical Internet of Things for Modern Healthcare

The integration of the Internet of Things (IoT) in healthcare has been a popular topic in recent years. This article provides a comprehensive review of the medical IoT for healthcare, emphasizing the state of the art, the enabling technologies to adopt virtuality and reality interaction, and human-centered communication for healthcare (the Metaverse, Extended Reality (XR), blockchain, Artificial Intelligence (AI), robotics). In particular, we assess the number of scientific articles and patents within the period 2015–2022. We then use the two-stage process following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and screening techniques. From that, the relations among the published papers can be visualized. This study examines the insights by evaluating the weights and connections of the nodes in the bibliometric networks. The reviewed papers showcase the rapid growth of IoT-related studies and intellectual property developments, reflecting the burgeoning interest and investment in this domain. As this paper delves into the network of interconnections between these works, it fosters a deeper understanding of the current state of IoT applications in healthcare and uncovers potential research gaps and areas for future exploration. This paper also provides a brief view of the role of IoT in healthcare research and application in combination with emerging technologies such as AI, blockchain, the IoT-enabled Metaverse, robotics, and cloud computing. The article can serve as a guideline and inspiration for both researchers and practitioners in the smart health service sector.

IoT-enabled solutions for Alzheimer's disease management: innovations and opportunities

Alzheimer's disease (AD) is a common neurological disorder characterised by the progressive shrinkage of brain tissue and the death of cells. Understanding how genetic and environmental factors interact to cause AD is challenging but crucial for effectively managing and treating this disease. Many personal, social, and economic impacts can be attributed to AD, making it a crucial area for research. This paper proposes using Internet of Things (IoT) technologies to assist people with Alzheimer's disease. IoT can potentially enhance people's quality of life and simplify daily activities. IoT applications in healthcare, smart homes, and patient tracking have been explored. Various sensors, devices, and software can be utilised to monitor patients' health status. By leveraging IoT, we can develop innovative solutions to address AD management challenges and improve the overall quality of patient care.

Internet of Things in Digital Health Care Research: A Bibliometric Analysis of the Recent Literature

ABSTRACT The Internet of Things (IoT) is expanding rapidly and can potentially be a huge data source. This study aimed to close the knowledge gap about possible IoT applications in health care. English-language academic publications from the Web of Science (WoS) were used for the present study. Bibliometric networks were used to analyze pertinent publications to examine the connections between authors, nations, and affiliations. Papers on digital health care have been published in more than 1195 sources, among which the most popular was the Journal of Medical Internet Research. Six top institutions were from the Netherlands, making the Vrije Universiteit Amsterdam the most productive institution after Australia and England. This study provides an overview of IoT-related research conducted in digital health care, helping academic researchers, policymakers, and practitioners to better understand the development of digital health care research.

Impact Of Iot Adoption And Application For Smart Healthcare

The Internet of Things (IoT) is a rapidly growing technology that is transforming the healthcare industry. IoT devices and applications are enabling the collection and analysis of patient data, facilitating remote monitoring and diagnosis, and improving the efficiency and effectiveness of healthcare delivery. This abstract explores the impact of IoT adoption and application for smart healthcare. The study highlights the various applications of IoT in healthcare, such as remote patient monitoring, real-time patient tracking, and medication management. The study reviews the benefits of IoT in healthcare, such as improved patient outcomes, reduced costs, and increased efficiency. The study also examines the challenges of implementing IoT in healthcare, such as data privacy and security concerns, interoperability issues, and regulatory compliance. The study concludes that IoT is transforming the healthcare industry, enabling a new era of smart healthcare delivery. The adoption of IoT in healthcare has the potential to improve patient outcomes, reduce costs, and increase the efficiency and effectiveness of healthcare delivery. However, the challenges of implementing IoT in healthcare cannot be ignored, and the industry must work to address these challenges while harnessing the benefits of this emerging technology. Overall, the potential impact of IoT adoption and application for smart healthcare is significant and holds great promise for the future of healthcare delivery.

An IoT-based interoperable architecture for wireless biomonitoring of patients with sensor patches

The alliance between the Internet of Things (IoT) and healthcare has the potential to improve healthcare assistance at different stages of care through distributed vital sign sensing, paving the way for domiciliary hospitalization. In this work, we propose an innovative design for an IoT-based interoperable healthcare system to wirelessly monitor and classify patient status. To support our research, we identify gaps, and discuss standards, protocols and technologies based on works that use relevant IoT applications in healthcare. The proposed architecture is centered on several low-energy unobtrusive sensors attached to the patients’ bodies, as well as their beds, which encompass data acquisition nodes linked to a smart gateway that aggregates data. The smart gateway is integrated with an existing hospital information system through the exchange of Electronic Health Records (EHR), making relevant patient data easily available to health professionals on systems which are familiar to them. A use case scenario is presented in order to fulfill functional and non-functional requirements and provide a better understanding of connection and communication between the distinct entities of the proposed architecture, which is based on Bluetooth Low Energy (BLE) technology at the data acquisition level, the Message Queuing Telemetry Transport (MQTT) protocol at the internal level, and on the Fast Healthcare Interoperability Resources (FHIR) standard at the higher level.

Understanding IoHT and Edge/Fog Computing Solutions for Smart In-Home Remote Healthcare

Healthcare information systems have been dominated by cloud technology and the Internet of Things (IoT) for decades today. In some urgent scenarios, we reveal a prevailing architectural framework that is based on fog/edge optimal computing approaches smart in-home remote healthcare solutions and architectures and recognize the challenges and requirements of IoHT devices for diverse utilization instances. Even with these upsides, conventional centralized access constraint confronts privacy problems and patient health data security. This study likewise constructs a “blockchain-enabled edge that computes” mechanism, through which smart contracts with the consensus protocol produced by Edge Intelligent Server are deployed to secure privacy topics and balance scalability in trustless surroundings. We expect this paper to be a significant guideline for the subsequent elaboration of fog/edge-based systems that compute solutions for smart in-home remote healthcare IoT applications. There will be a change of paradigm from “hospital-based” to “distributed patient in-home healthcare”.

Emerging IoT Applications in Sustainable Smart Cities for COVID-19: Network Security and Data Preservation Challenges with Future Directions.

COVID-19 is a global infectious disease that can be easily spread by the contiguity of infected people. To prevent from COVID-19 and reduce its impact in sustainable smart cities, the global research communities are working relentlessly by harnessing the emerging technologies to develop the safest diagnosis, evaluation, and treatment procedures, and Internet of Things (IoT) is one of the pioneers among them. IoT can perform a pivotal role to diminish its immense contagious rate by suitable utilization in emerging healthcare IoT applications in sustainable smart cities. Therefore, the focus of this paper is to outline a survey of the emerging healthcare IoT applications practiced in the perspective of COVID-19 pandemic in terms of network architecture security, trustworthiness, authentication, and data preservation followed by identifying existing challenges to set the future research directions. The salient contributions of this work deal with the accomplishment of a detailed and comprehensive literature review of COVID-19 starting from 2019 through 2021 in the context of emerging healthcare IoT technology. In addition, we extend the correlated contributions of this work by highlighting the weak aspects of the existing emerging healthcare IoT applications, security of different network layers and secure communication environment followed by some associated requirements to address these challenges. Moreover, we also identify future research directions in sustainable smart cities for emerging healthcare IoT utilization in the context of COVID-19 with the most productive results and least network implementation costs.

Determining the Degree of Characteristics for Internet of Healthcare Devices Using Fuzzy ANP

With the revolution in Internet and digital technology, every organization is adopting digital things to carry out their day-to-day activities. Internet of Things (IoT) is a concept that is used to connect various devices over the Internet to increase the production and quality of service, deliver a huge amount of data in seconds, and automate the processes. IoT implementation in the health sector has changed the typical setup into smart and intelligent setup. With the “smart” and “Intelligent” abilities of IoT devices such as sensors and with the collaboration of humans and computers, physical processes can be monitored and, based on the received data, optimal decisions can ultimately be taken. IoT applications in healthcare will increase flexibility, patient’s care, quality of health, and control of diseases. As the IoT combines various heterogeneous devices and the inappropriate determination of the degree of characteristics for Internet of healthcare (IoH) devices may affect the efficiency of services, this research is carried out with the use of decision support system and application of fuzzy analytic network process (fuzzy ANP) was used for optimal determination of the degree of characteristics for IoH devices based on unique properties which would greatly increase the efficiency of industry. The experimental results are efficient and show the usefulness of the approach.

Energy‐efficient fuzzy logic‐based cross‐layer hierarchical routing protocol for wireless Internet‐of‐Things sensor networks

SummaryWith rapid increase of the advancement in wireless technologies, the world is now moving toward smart wireless devices which require the communication and collaboration of the Internet of Things (IoT). The smart devices, which called sensor nodes, enhance the use of the IoT as the essential components of today's smart world. Therefore, the wireless sensor network (WSN) is the most widely used technology enabling IoT scenarios. Using the technology, a huge volume of data needs to be tackled and transmitted carefully via the internet. Since the lifetime of the network is related to the reduced battery capacity of the connected wireless sensor nodes, extending the network lifetime is the main goal to be achieved. Clustering algorithm has a significant effect on the system lifetime for the IoT‐enabled WSN applications. In this paper, a fuzzy‐based routing protocol (FRP‐LEACH) and a cross‐layer routing protocol (CLRP‐LEACH) are proposed based on the clustering topology. The proposed algorithms are designed for the healthcare IoT applications. These algorithms offer several advanced cloud‐based services and facilities to serve patients more effectually and protect the medical and paramedical framework from pandemic illness like the ongoing COVID‐19 pandemic. To address the above‐mentioned concerns, an energy efficient routing protocol based on fuzzy logic is proposed to optimize wireless Internet‐of‐Things sensor networks performance. Simulation results are presented, and they show that the proposed algorithms outperform current cluster based‐routing protocols using a fuzzy logic algorithm in terms of generating stable clusters and extending the network lifetime.

Edge-Based Hybrid System Implementation for Long-Range Safety and Healthcare IoT Applications

In many conventional Internet-of-Things (IoT) applications, data are transferred directly from the sensor network to the cloud via a gateway for further data processing. However, this typical usage of a gateway is not suitable for every application. For short-range IoT wireless protocols [e.g., Bluetooth low energy (BLE)], multiple gateways are required to achieve broader coverage, which is inconvenient. In this article, an edge-based hybrid network system architecture is presented. The proposed system consists of hybrid routers and an IoT gateway. The router supports two wireless protocols, BLE and long range (LoRa), and is equipped with a solar energy harvester to extend the router's lifetime. It can extend the coverage of short-range BLE network by utilizing LoRa wireless technology, and support fundamental edge computing tasks such as preliminary data processing. The IoT gateway can support multiple IoT protocols, including LoRa, BLE, and XBee. It can perform more advanced edge computing tasks, such as data filtering, storage, processing, user interface, and cloud connection. Three case studies incorporating a wearable safety monitoring sensor network, a healthcare monitoring application, and a smart hospital application are studied with the proposed edge network system to demonstrate its promising capabilities to support IoT applications. Experimental evaluations indicate that by processing data at the edge, the minimal delay is only 11.5 ms. Furthermore, with the hybrid LoRa network implementation, the BLE network can be extended to 2.4 km.

Edge Intelligence and Internet of Things in Healthcare: A Survey

With the advent of new technologies and the fast pace of human life, patients today require a sophisticated and advanced smart healthcare framework that is tailored to suit their individual health requirements. Along with 5G and state-of-the-art smart Internet of Things (IoT) sensors, edge computing provides intelligent, real-time healthcare solutions that satisfy energy consumption and latency criteria. Earlier surveys on smart healthcare systems were centered on cloud and fog computing architectures, security, and authentication, and the types of sensors and devices used in edge computing frameworks. They did not focus on the healthcare IoT applications deployed within edge computing architectures. The first purpose of this study is to analyze the existing and evolving edge computing architectures and techniques for smart healthcare and recognize the demands and challenges of different application scenarios. We examine edge intelligence that targets health data classification with the tracking and identification of vital signs using state-of-the-art deep learning techniques. This study also presents a comprehensive analysis of the use of cutting-edge artificial intelligence-based classification and prediction techniques employed for edge intelligence. Even with its many advantages, edge intelligence poses challenges related to computational complexity and security. To offer a higher quality of life to patients, potential research recommendations for improving edge computing services for healthcare are identified in this study. This study also offers a brief overview of the general usage of IoT solutions in edge platforms for medical treatment and healthcare.

Clinical Perspective on Internet of Things Applications for Care of the Elderly

The elderly population is increasing globally, putting more pressure on aged care and public health systems. To address this issue and help increase the independence of older people, different digital technologies, including the Internet of things (IoT), can play an important role. Although there has been an increase in the number of studies in this field, most of them concentrate on IoT applications in elderly care from a technology perspective, with very little contribution from the clinical side. Therefore, this paper aims to investigate and identify the available IoT applications and their clinical utility for common diseases in elderly people. The results of this study could be useful for information technology professionals in developing and understanding the clinical requirements for IoT applications in healthcare for older people. Clinicians will also be informed about the clinical possibilities of using IoT devices in this area. Based on our findings, future research should focus on enhancing the clinical utility of current IoT applications in different settings and on developing new applications to support practitioners and older people.

Edge-Assisted Control for Healthcare Internet of Things

Recent advances in pervasive Internet of Things technologies and edge computing have opened new avenues for development of ubiquitous health monitoring applications. Delivering an acceptable level of usability and accuracy for these healthcare Internet of Things applications requires optimization of both system-driven and data-driven aspects, which are typically done in a disjoint manner. Although decoupled optimization of these processes yields local optima at each level, synergistic coupling of the system and data levels can lead to a holistic solution opening new opportunities for optimization. In this article, we present an edge-assisted resource manager that dynamically controls the fidelity and duration of sensing w.r.t. changes in the patient’s activity and health state, thus fine-tuning the trade-off between energy efficiency and measurement accuracy. The cornerstone of our proposed solution is an intelligent low-latency real-time controller implemented at the edge layer that detects abnormalities in the patient’s condition and accordingly adjusts the sensing parameters of a reconfigurable wireless sensor node. We assess the efficiency of our proposed system via a case study of the photoplethysmography-based medical early warning score system. Our experiments on a real full hardware-software early warning score system reveal up to 49% power savings while maintaining the accuracy of the sensory data.

A novel word similarity measure method for IoT-enabled Healthcare applications

With the development of the Internet of Things (IoT), Natural Language Processing(NLP) has become a key part of IoT applications in Healthcare. NLP is bringing a revolutionary shift to Healthcare, powered by rapid progress of NLP analytics techniques and increasing availability of Healthcare data. Therefore, using NLP solution for IoT enable Healthcare application is an urgent and valuable task. Word similarity measurement is the basis of semantic analysis, which can be applied to translation and disambiguation of medical terms, prescription analysis, medical question and answer systems, diagnostic assistance, etc. Previous similarity measures have mainly focused on either knowledge-graph-based or word-embedding-based methods, which suffer from two problems: (1) word-embedding-based methods have difficulty discriminating words with approximately the same surrounding context; and (2) knowledge-graph-based methods do not contain multiexpression words or named entities and cannot generally converge for large-scale and updated words. To solve these two problems, this paper proposes a novel method that combines knowledge-graph-based and word-embedding-based similarity measures via word entropy. An experiment is conducted on five public datasets (R&G, M&C, WS353, WS353-Sim and SimLex). The experimental results show that the proposed method achieves significant improvements over other word similarity measures in terms of the correlation coefficient.

Exploratory Analysis of Internet of Things (IoT) in Healthcare: A Topic Modelling & Co-citation Approaches

ABSTRACT Internet of Things (IoT) have made a significant impact in healthcare domain. The purpose of this study is to unravel key themes latent in the academic literature on IoT applications in healthcare. Using topic modeling and author co-citation techniques, we identified five dominant clusters of research. Our results show that research in healthcare IoT has mainly focused on the technical aspects with little attention to social concerns. Our paper provides directions for future research.

Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review.

Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressed.