Things In Healthcare Research Articles

Applying Internet of Things in Healthcare: A Survey

Applying Internet of Things in Healthcare: A Survey

Data Analysis and Interpretation in IoT-Based Systems for Critical Medical Services and Healthcare Applications

The use of Internet of things in health care is a major breakthrough as it can help us save a lot of lives that can be prevented because of prolonged commute distance to the hospital. We have improvised on pre-existing models to create this model. We were successfully able to achieve results on a small scale by transmitting relays of data over a Wi-Fi network. Our model will help reduce the travel time, as well as send data to prior to the hospitals so they can take necessary precautions to attend to the patient. We have come up with a two-step process to achieve. (1) Create a green corridor for an ambulance. (2) Send the patient details (blood group, the reason for emergency, pulse rate, etc.) to the respective hospital.

A Survey on Healthcare Systems using Internet of Things

The Internet of Things allows things to become active users, facilitating interaction with things and the sharing of data between them. The most interesting issue in the science world, the public sector and industry in the IoT is unavoidable. It ensures a seamless relationship between doctors and patients that results in medical treatment with high quality results. This is accomplished by constant surveillance of patients through the use of sensors. The collected data is registered for potential uses and used for analytics. The analytical approach offers the opportunity for disease detection in healthcare results. This paper concerns the Internet of Things in healthcare and explores the different algorithms used in it. The system involved in analytics of healthcare and data sources involved in analytics are further clarified. Finally this paper demonstrates the Internet of Things and Big Data survey of healthcare systems with a reference table.

Exploring internet of healthcare things for establishing an integrated care link system in the healthcare industry

With the ageing population all over the world, long-term care services, such as nursing care, are essential to provide care and treatments to elderly patients in the community. During the nursing care services, elderly patients who live in the nursing homes require to be treated and consulted in a number of healthcare organisations, for example hospitals, mental health centres and rehabilitation centres. Currently, the data management for the elderly is relatively centralised to establish their own electronic medical records and protected health information without decision support functionalities. The community and healthcare industry are eager to develop a safe and comprehensive system to provide adequate healthcare services and monitoring to the elderly. In this study, an internet of healthcare things (IoHT)-based care link system (IoHT-CLS) is proposed, which provides a structured framework on integrating IoHT and artificial intelligence (AI) to generate a one-stop solution for managing elderly’s healthcare facilities. The elderly can be effectively linked into the integrated IoHT system by using various sensing and data collection technologies. The collected data are further processed by means of the adaptive neuro-fuzzy inference system and case-based reasoning to provide the functionalities of risk management and customised elderly service programmes for the elderly care institutions. Consequently, this study contributes to the healthcare management through the enhancement of service quality in the community.

Recurrent Model for Automatic Detection Cardiac Arrhythmia on the Internet of Healthcare Things

With the growing prevalence of the Internet of Health Things (IoHT), there is an increasing need for reliable and precise categorization of electrocardiogram (ECG) indications for the early detection of cardiovascular diseases. In this research, we propose a machine learning approach for ECG classification in IoHT applications. Our solution use wavelet transforms to clean the ECG records before passing them to the model. Then, a stack of long short-term memory (LSTM) cells is built to learn the temporal interrelations in the ECG signals and make accurate predictions. We assessed the performance of our model on a publicly available dataset of ECG signals, achieving an overall accuracy of 97.5%. The experimental findings demonstrate that our models can effectively classify ECG signals in IoHT applications, providing a valuable tool for the early discovery of vascular diseases. Furthermore, our model can be certainly incorporated into IoHT systems, providing a reliable and efficient solution for ECG classification.

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based security and privacy systems for several types of IoT applications, including, Industrial IoT, Edge Computing, Internet of Drones, Internet of Healthcare Things, Internet of Vehicles, etc. Second, the use of federated learning with blockchain and malware/intrusion detection systems for IoT applications is discussed. Then, we review the vulnerabilities in federated learning-based security and privacy systems. Finally, we provide an experimental analysis of federated deep learning with three deep learning approaches, namely, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). For each deep learning model, we study the performance of centralized and federated learning under three new real IoT traffic datasets, namely, the Bot-IoT dataset, the MQTTset dataset, and the TON_IoT dataset. The goal of this article is to provide important information on federated deep learning approaches with emerging technologies for cyber security. In addition, it demonstrates that federated deep learning approaches outperform the classic/centralized versions of machine learning (non-federated learning) in assuring the privacy of IoT device data and provide the higher accuracy in detecting attacks.

IoHT-MBA: An Internet of Healthcare Things (IoHT) Platform based on Microservice and Brokerless Architecture

Internet of Thing (IoT), currently, is one of the technology trends that are most interested. IoT can be divided into five main areas including: Health-care, Environmental, Smart city, Commercial and Industrial. The IoHT-MBA Platform is considered the backbone of every IoT architecture, so the optimal design of the IoHT-MBA Platform is essential issue, which should be carefully considered in the different aspects. Although, IoT is applied in multiple domains, however, there are still three main features that are challenge to improve: i) data collection, ii) users, devices management, and iii) remote device control. Today’s medical IoT systems, often too focused on the big data or access control aspects of participants, but not focused on collecting data accurately, quickly, and efficiently; power redundancy and system expansion. This is very important for the medical sector - which always prioritizes the availability of data for therapeutic purposes over other aspects. In this paper, we introduce the IoHT Platform for Healthcare environment which is designed by microservice and brokerless architecture, focusing strongly on the three aforementioned characteristics. In addition, our IoHT Platform considers the five other issues including (1) the limited processing capacity of the devices, (2) energy saving for the device, (3) speed and accurate of the data collection, (4) security mechanisms and (5) scalability of the system. Also, in order for the IoHT Platform to be suitable for the field of health monitoring, we also add realtime alerts for the medical team. In the evaluation section, moreover, we describe the evaluation to prove the effectiveness of the proposed IoHT Platform (i.e. the proof-of-concept) in the performance, non-error, and non affected by geographical distance. Finally, a complete code solution is publicized on the authors’ GitHub repository to engage further reproducibility and improvement.

Fog-cloud assisted framework for Heterogeneous Internet of Healthcare Things

Abstract Rapid advancements in the fields of IoT based communication allows seamless connection among several devices allowing them to communicate with each other. This technological integration unlocks the new possibilities in different disciplines including healthcare. Internet of Healthcare Things (IoHT) enabled framework has been followed to provide improved and reliable services and reduce healthcare costs. As heterogeneous and critical healthcare data needs fast response, data analytics of it is a complex problem. To address this challenge, the paper proposes an architecture where data will be forwarded to the fog or cloud layer depending on the traffic type. To handle this issue efficiently, load balancing is done for selecting the appropriate fog/cloud to forward the data from device. Moreover, real time and conventional data are treated differently by the algorithm. To take the forwarding decision software-defined networking has been adapted which ensures to perform the resource allocation and load balancing efficiently. The proposed work focus to reduce the latency and packet loss while maximizing the throughput in e-healthcare systems.

Performance Evaluation of an Internet of Healthcare Things for Medical Monitoring Using M/M/c/K Queuing Models

Due to the non-stop and rapid spreading of virus pandemics all over the world, traditional healthcare monitoring capabilities of hospitals and/or medical centers are under a severe over-load. Modern computing infrastructures with the harmony of various layers of computing paradigms (e.g., cloud/fog/edge computing) for healthcare monitoring are apparently the essential computing backbone that help access and process instantly the medical data of every single patient at the very edge of the healthcare system to combat with global or regional virus contagion. Previous studies proposed different computing system architectures for healthcare monitoring but few works considered the evaluation of pure performance of medical data transmission in a comprehensive manner. In this paper, we proposed an M/M/c/K queuing network model for the performance evaluation of an Internet of Healthcare Things (IoHT) infrastructure in association with a three layer cloud/fog/edge computing continuum. The model considers a life cycle of medical data from body-attached IoT sensors in edge layer all the way to local clients (e.g., local medical doctors, physicians) through fog layer and to remote clients (e.g., medical professionals, patient’s family members) through cloud layer. Furthermore, we also explore the impact of the alteration in system configuration and computing capability of computing layers in two scenarios on various performance metrics. Critical performance metrics related to quality of service are evaluated in a comprehensive manner, such as (i) mean response time of medical data transmission to fog (local) clients and to cloud (remote) clients, (ii) utilization of cloud/fog/edge computing layers, (iii) service throughput, (iv) number of medical messages in a period of time, and (v) drop rate. The simulation results pinpoint bottle-neck parameters and configurations of the IoHT infrastructure’s system architecture in relation to the frequency of medical data collection for health check of patients. Thus, the findings of this study can help improve medical administration in hospitals and healthcare centers and help design computing infrastructures in accordance for medical monitoring in the severe circumstances of virus pandemics.

Secure and failure hybrid delay enabled a lightweight RPC and SHDS schemes in Industry 4.0 aware IIoHT enabled fog computing.

These days, the Industrial Internet of Healthcare Things (IIT) enabled applications have been growing progressively in practice. These applications are ubiquitous and run onto the different computing nodes for healthcare goals. The applications have these tasks such as online healthcare monitoring, live heartbeat streaming, and blood pressure monitoring and need a lot of resources for execution. In IIoHT, remote procedure call (RPC) mechanism-based applications have been widely designed with the network and computational delay constraints to run healthcare applications. However, there are many requirements of IIoHT applications such as security, network and computation, and failure efficient RPC with optimizing the quality of services of applications. In this study, the work devised the lightweight RPC mechanism for IIoHT applications and considered the hybrid constraints in the system. The study suggests the secure hybrid delay scheme (SHDS), which schedules all healthcare workloads under their deadlines. For the scheduling problem, the study formulated this problem based on linear integer programming, where all constraints are integer, as shown in the mathematical model. Simulation results show that the proposed SHDS scheme and lightweight RPC outperformed the hybrid for IIoHT applications and minimized 50% delays compared to existing RPC and their schemes.

Device Security Assessment of Internet of Healthcare Things

Device Security Assessment of Internet of Healthcare Things

The Role of the Internet of Things in Healthcare: Future Trends and Challenges

Background and ObjectiveWith the recent advances in the Internet of Things (IoT), the field has become more and more developed in healthcare. The Internet of things will help physicians and hospital staff perform their duties comfortably and intelligently. With the latest advanced technologies, most of the challenges of using IoT have been resolved, and this technology can be a great revolution and has many benefits in the future of digital. Healthcare is one of the most useful areas for IoT use. The most important application of IoT is to monitor and make quick decisions in critical situations. Thanks to this technology-based treatment approach, there is an unprecedented opportunity to better the quality and productivity of treatments and better the patient's well-being and better government funding. MethodsIn this paper, we provide a comprehensive overview of the primary uses of IoT in healthcare. We used the Systematic Literature Review (SLR) method to analyze and comparison articles published in this field between 2015 and March 2020. ResultsA comprehensive taxonomy is presented based on the contents of the articles under study. In this article, a brief overview of selected articles based on research questions is given and highlights the most critical challenges and case studies for the future use of IoT in healthcare. ConclusionsAccording to a detailed study of the 89 articles and a glimpse into about 208 articles, challenges and future trends in healthcare have been identified.

Design and Implementation of an Internet of Healthcare Things System for Respiratory Diseases

Internet of Things (IoT) paradigm broadens to several research fields. Thus, Wireless Body Area Network (WBAN) has been adopted as a standard to create an IoT scheme implemented on healthcare system. Furthermore, IoT can be employed to measure several diseases including stroke, diabetes, as well as respiratory diseases to monitor patient condition and environment change that might be harmful for patients. Implementation and realization of IoT for monitoring respiratory diseases is needed since in Taiwan the risk to get more severe symptoms by those diseases is relatively high considering the air pollution that is getting higher. In this study, IoT system is built based on the integration of several independent applications. In addition, our scheme consists of four main components such as environment sensing box, patient monitoring tools, android apps, and web Graphical User Interface (GUI). Web GUI is useful for health practitioners such as doctors and nurses to monitor the condition of patients obtained by patient monitoring tools. Moreover, patients and doctors can assess the status of weather and environmental condition whether it is safe or harmful for patients. Finally, android based apps is necessary for patients to connect all of schemes as well as monitoring all conditions including health and environmental status. The assessment of our tools indicates that the implemented scheme is consistent enough shown by low Root Mean Square Error (RMSE) and Mean Average Percentage Error (MAPE) achieved by our IoT system.

Dynamic Contract Design for Federated Learning in Smart Healthcare Applications

Currently, the data collected by the Internet of Healthcare Things, i.e., healthcare oriented Internet of Things (IoT), still rely on cloud-based centralized data aggregation and processing. To reduce the need for transmission of data to the cloud, the edge computing architecture may be adopted to facilitate machine learning at the edge of the network through leveraging on the amassed computation resources of pervasive IoT devices. In this article, federated learning (FL) is proposed to enable privacy-preserving collaborative model training at the edge of the network across distributed IoT users. However, the users in the FL network may have different willingness to participate (WTP), a hidden information unknown to the model owner. Furthermore, the development of healthcare applications typically requires sustainable user participation, e.g., for the continuous collection of data during which a user’s WTP may change over time. As such, we leverage on the dynamic contract design to consider a two-period incentive mechanism that satisfies the intertemporal incentive compatibility (IIC), such that the self-revealing mechanism of the contract holds across both periods. The performance evaluation shows that our contract design satisfies the IIC constraints and derives greater profits than that of the uniform pricing scheme, thus validating its effectiveness in mitigating the adverse impacts of the information asymmetry.

Big data, machine learning, and artificial intelligence: a field guide for neurosurgeons.

Big data has transformed into a trend phrase in healthcare and neurosurgery, becoming a pervasive and inescapable phrase in everyday life. The upsurge in big data applications is a direct consequence of the drastic boom in information technology as well as the growing number of internet-connected devices called the Internet of Things in healthcare. Compared with business, marketing, and other sectors, healthcare applications are lagging due to a lack of technical knowledge among healthcare workers, technological limitations in acquiring and analyzing the data, and improper governance of healthcare big data. Despite these limitations, the medical literature is flooded with big data-related articles, and most of these are filled with abstruse terminologies such as machine learning, artificial intelligence, artificial neural network, and algorithm. Many of the recent articles are restricted to neurosurgical registries, creating a false impression that big data is synonymous with registries. Others advocate that the utilization of big data will be the panacea to all healthcare problems and research in the future. Without a proper understanding of these principles, it becomes easy to get lost without the ability to differentiate hype from reality. To that end, the authors give a brief narrative of big data analysis in neurosurgery and review its applications, limitations, and the challenges it presents for neurosurgeons and healthcare professionals naive to this field. Awareness of these basic concepts will allow neurosurgeons to understand the literature regarding big data, enabling them to make better decisions and deliver personalized care.

Internet of Things in Animal Healthcare (IoTAH): Review of Recent Advancements in Architecture, Sensing Technologies and Real-Time Monitoring

In recent days, the Internet of Things (IoT) used to connect many devices and communicate with each other, which created a greater impact on animal healthcare systems. IoT devices are in the form of wearable’s that have been used to track the activities of humans. Now, the wearable devices are used in monitoring the activities of the animals. Internet of Things in Animal Healthcare (IoTAH) uses the biosensors and software for monitoring and maintaining the animal health records. These kinds of technologies make a precise health status and sickness projection which are most effective in humans but it can be applied to animals with few changes. Some of those recent technologies acquired the importance of their use in animal healthcare and development. The integration of available medical sensors creates a connected digital platform that empowers the connectivity with pets and livestock with improved efficiency. This article describes the scope of biosensors, computing, communicating, and wearable technologies available for animals. The main intention of this article is to review the recent advancements in the field of animal healthcare which includes domestic, farm, and wild animals. This article reviews the smart technologies available for various categories of animals. The outcomes of this survey are expected to improve the future research and development of animal welfare systems.

The Role of the Internet of Things in Health Care: A Systematic and Comprehensive Study

The Internet of Things (IoT) is becoming an emerging trend and has significant potential to replace other technologies, where researchers consider it as the future of the internet. It has given tremendous support and become the building blocks in the development of important cyber-physical systems and it is being severed in a variety of application domains, including healthcare. A methodological evolution of the Internet of Things, enabled it to extend to the physical world beyond the electronic world by connecting miscellaneous devices through the internet, thus making everything is connected. In recent years it has gained higher attention for its potential to alleviate the strain on the healthcare sector caused by the rising and aging population along with the increase in chronic diseases and global pandemics. This paper surveys about various usages of IoT healthcare technologies and reviews the state of the art services and applications, recent trends in IoT based healthcare solutions, and various challenges posed including security and privacy issues, which researchers, service providers and end users need to pay higher attention. Further, this paper discusses how innovative IoT enabled technologies like cloud computing, fog computing, blockchain, and big data can be used to leverage modern healthcare facilities and mitigate the burden on healthcare resources.

The Internet of Things in Healthcare. An Overview

The Internet of Things in Healthcare. An Overview

Portable Smart-Space Research Interface to Predetermine Environment Acoustics for Cochlear implant and Hearing aid users with CCi-MOBILE.

Internet of things (IoT) in healthcare, has effi-ciently accelerated medical monitoring and assessment through the real-time analysis of collected data. Hence, to support the hearing-impaired community with better calibrations to their clinical processors and hearing aids, a portable smart space interface - AURIS has been developed by the Cochlear Implant Processing Lab (CILab) at UT-Dallas. The proposed Auris interface periodically samples the acoustic space, and through a learn vs test phase, builds a Gaussian mixture model for each specific environmental locations. An effective connection is established by the Auris interface with the CRSS CCi-Mobile research platform through an android app to fine tune the con-figuration settings for cochlear implant (CI) or hearing aid (HA) users entering the room/location. Baseline objective evaluations have been performed in diverse naturalistic locations using 12 hours of audio data. The performance metrics is determined by a verified wireless communication, along with estimated acoustic environment knowledge and room classification at greater than 90% accuracy.

Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things

Along with the advancement of several emerging computing paradigms and technologies, such as cloud computing, mobile computing, artificial intelligence, and big data, Internet of Things (IoT) technologies have been applied in a variety of fields. In particular, the Internet of Healthcare Things (IoHT) is becoming increasingly important in human activity recognition (HAR) due to the rapid development of wearable and mobile devices. In this article, we focus on the deep-learning-enhanced HAR in IoHT environments. A semisupervised deep learning framework is designed and built for more accurate HAR, which efficiently uses and analyzes the weakly labeled sensor data to train the classifier learning model. To better solve the problem of the inadequately labeled sample, an intelligent autolabeling scheme based on deep $Q$ -network (DQN) is developed with a newly designed distance-based reward rule which can improve the learning efficiency in IoT environments. A multisensor based data fusion mechanism is then developed to seamlessly integrate the on-body sensor data, context sensor data, and personal profile data together, and a long short-term memory (LSTM)-based classification method is proposed to identify fine-grained patterns according to the high-level features contextually extracted from the sequential motion data. Finally, experiments and evaluations are conducted to demonstrate the usefulness and effectiveness of the proposed method using real-world data.