Taxonomic Exploration of Healthcare IoT

Taxonomic Exploration of Healthcare IoT: History

Please note this is an old version of this entry, which may differ significantly from the current revision.

Subjects: Computer Science, Information Systems

Contributor:

Lutifa Alashlam

An Internet of things (IoT) ecosystem is a fast-developing network in which users can connect a heterogeneity of physical and virtual devices, including customized healthcare areas. As medical resources are scarce, populations are aging with chronic diseases and require remote monitoring, medical expenses are rising, and telemedicine is being demanded in developing nations, the IoT is an attractive topic in healthcare. Through the IoT, people can enjoy better health and diminish pressure on sanitary systems.

Internet of Things
e-healthcare
smart environment

1. Introduction

Today’s world has faced numerous obstacles relating to chronic disease public health issues caused by hazardous viruses such as COVID-19 [1]. The increase in health-related issues coupled with rising healthcare expenditures has encouraged people, especially the elderly and disabled, to manage their health remotely. Many industries, such as remote and smart healthcare systems, have benefitted from the IoT in the past few years as a network of interconnected objects [2]. As part of the IoT, popular technologies like wireless body area networks (WBANs), wireless sensor networks (WSNs), and radio frequency identification (RFID) are used to transpose data to the cloud, which can then be analyzed and used for instantaneous decision-making [3,4].

It is possible to make health management systems more customized, provident, and cost-effective by using the IoT, including mental health services and the worldwide pandemic [5,6,7]. This strategy categorizes IoT implementation in healthcare into three categories: tracking people and other objects (staff, medical teams, and patients), person authentication and identity, and autonomous data sensing and gathering. IoT-based health monitoring, like WBAN, enables hospitals to prevent and manage hospital infections, manage emergencies, and dispense post-discharge care anywhere. As a result, the healthcare industry is completely redefined by the IoT in terms of its devices, applications, and users [8,9]. Therefore, healthcare environments can be dramatically revolutionized by leveraging IoT technologies such as connected medical sensors or special medical devices [10,11,12].

Through the use of HIoT technology in healthcare systems, medical devices are connected to the Internet so that appropriate therapeutic strategies can be developed for patients. Eldercare supervision, telemonitoring, teleconsultations, and computer-assisted rehabilitation are just some telehealth services offered by this technology [13]. Internet of medical things and HIoT are abbreviations that are frequently used interchangeably to refer to the integration of medical applications and equipment that can be linked to systems of health care information technology [14,15]. Meanwhile, big data combined with IoT can be used to support sanitary systems that are more effective in managing healthcare operations. The analysis of healthcare practices can now be prescriptive, autonomous, and predictive thanks to big data analytics [16].

2. Background

2.1. Internet of Things

Ashton et al. defined the phrase “Internet of Things” for the first time in the context of supply management [25]. Now, several IoT concepts are possible, including addressing devices on a network and ensuring that they are uniquely identified. These devices communicate with computers to transpose data and extract critical information that allows them to dispense suitable services more effectively. In other words, the IoT is a collection of numerous hardware components, such as different types of sensors and actuators, that connect to and communicate with one another online.

A typical IoT ecosystem also contains cloud interfaces, complicated algorithms, sensors, communication interfaces, and privacy-protecting algorithms. Data collection from a range of devices is the responsibility of sensors. Network and communication infrastructures are also dispensed by RFID and WSN technologies, and sophisticated algorithms are used to process and handle data. Users can ingress various services at once thanks to the cloud environment’s capacity for numerous client/server requests. Fog computing addresses these problems and enables instantaneous analysis and rapid decision-making near users by eliminating latency, dependability, resource constraints, and other problems associated with cloud computing.

Moreover, IoT ecosystems feature communication interfaces, sensors, sophisticated algorithms, and cloud interfaces [26,27]. Data collection from a range of devices is the responsibility of sensors. Network and communication infrastructures are also dispensed by RFID and WSN technologies, and sophisticated algorithms are used to process and handle data. Due to problems with latency, dependability, resource constraints, and other aspects of cloud computing, fog computing was created to circumvent these problems and execute the same applications anywhere near users with instantaneous analysis and swift decision-making capabilities [28].

Although IoT has advanced dramatically, it is still in its nascent stages. There are numerous study concerns such as standardisation, device heterogeneity, scalability, security, and privacy [29,30]. A unique issue in this industry is the interoperability of smart devices, which enables the integration of diverse devices and multi-vendor systems. Furthermore, a key element of IoT is the low-cost interoperability of smart objects, which will soon enable clients to continue working with numerous providers [31,32,33]. IoT can also help lessen construction costs, simplify organizational infrastructure challenges, and support diverse infrastructure.

Nowadays, the IoT paradigm encompasses a wide range of applications such as transportation, smart cities, monitoring, healthcare, and so on [34,35]. Despite the variety of IoT devices, it is simple to link, collect, and compare data in IoT applications like smart cities and smart homes in order to adjust to people’s needs. The adoption of the IoT paradigm, for instance, has the potential to revolutionize sanitary systems in the healthcare sector [36]. It can be useful for telemonitoring in the hospital, as well as at home for elderly people with chronic conditions [37,38]. The application of this technology will help healthcare systems in the future to dispense high-quality care at low hospitalisation costs, with diminished response times in detecting anomalies and longer life spans.

2.2. Healthcare

As well as other factors related to the pandemic, such as high prices, long distances, and quarantine requirements, the world is currently shaped by epidemics and infectious diseases spreading more widely, including COVID-19. Many elderly and disabled people suffer from chronic diseases, so it is difficult, if not impossible, for them to go to medical centres [39]. A practical, comprehensive, computer-aided technology is therefore essential to offer patients long-term care and remote medical monitoring while minimizing financial burden.

Through the analysis of large amounts of data, IoT has revolutionized sanitary systems, turning them into intelligent and predictive systems. IoT devices have been connected to record patients’ physiological data instantaneously, including blood glucose levels, temperature monitors, and other crucial information. Patients will benefit from novel medical services, including early diagnosis and continuous monitoring of serious diseases [40,41]. The IoT has several applications in the field of health care, including remote clinical monitoring, assisted living, chronic disease management, and preventative treatment. In addition, there are a number of IoT applications in healthcare, including home healthcare, mobile health, and e-healthcare.

As a result, IoT technology has allowed healthcare processes to be managed smartly, and self-care is possible. To prevent pharmaceutical errors and human error, some events can be identified, such as seizures, falls due to Parkinson’s disease, stroke rehabilitation, neurologic monitoring, and heart monitoring [42,43,44]. However, further hurdles remain to be overcome in order to produce successful and safe healthcare applications [45,46]. Numerous security protocols are available today to preserve data from assaults and threats.

The perception layer, networking layer, middleware layer, and application layer make up the HIoT system’s prominent basic four-layered architecture. The following are the explanations of the layers.

Perception Layer: At the bottom of the hierarchy, we have this layer, which we may refer to as ’hardware’ or ’physical’. By collecting and signalling data, this layer prepares data for transmission to the network layer.
Network Layer: In this layer, all smart devices are connected, and health data can be exchanged among them. The cutting-edge technologies used by this layer allow patients to securely transmit and receive health data from the base station.
Middleware Layer: In this layer, services are dispensed with names and addresses associated with requests. Non-homogeneous items can be used with HIoT applications without requiring peculiar equipment platforms. Health data are collected from the network layer and cached here.
Application Layer: Data from other layers are analyzed and combined in this layer to dispense healthcare services. Healthcare services can be dispensed at this layer to meet the needs of patients. In this layer, graphs, business models, and flowcharts that control all activities and healthcare services can be produced. HIoT systems cannot succeed without technological innovations, business models, and appropriate business models.

An IoT-based healthcare system known as HIoT also consists of three key phases of a workflow, namely data creation, data processing, and information consumption. The phases mentioned are described as follows.

(1): Data generation: The process of generating the required data involves using a variety of sensors, medical devices, and even direct data entry by patients or other involved healthcare teams. The perception layer is used for this phase, while the network layer is used to transpose the data collected.
(2): Data processing: In the data processing stage, collected data are analysed using well-known mechanisms including machine learning techniques and data analysis tools. Through the middleware layer, this stage is completed.
(3): Information consuming: In the information consumption phase, any decisions that medical teams must make on behalf of patients can be made utilizing the outputs and analytics from the data processing phase. This analytical information can even be used to activate the actuators. The application and business layers are used for this phase.

For greater understanding, the aforementioned processes are depicted in Figure 1 in a layered IoT architecture of a healthcare ecosystem. Wearable sensors are used to gather the user’s vital signs, as seen in this picture. An infrared link is then used to transmit the data to the smartphone. Finally, the data are communicated from the mobile device to the server across a network, such as an advanced fifth-generation network. By pressing a button, the database server receives information from sensors or the patient’s details in an instantaneous way [47].

Figure 1. Layered architecture of IoT in healthcare.

Other information is gathered from healthcare facilities and stored in the database, such as physician opinions and medical analytics. The intuitive engine of the system then studies the data that have been stored in the database to determine whether any aberrant data have occurred. An alarm is transmitted to the patient or doctor when the system recognizes an abnormal situation, and the patient is admitted to the hospital if the alarm is accepted. As a consequence of recent improvements in healthcare systems and digital communications, it is now feasible to connect to remote healthcare services from any location, providing patients with the greatest options for personalized healthcare services.

2.3. Criteria for Evaluating HIoT Approaches

The criteria used to evaluate the suggested HIoT techniques are described in this section.

(1): Security and Privacy: Smart healthcare systems must prioritise security and privacy in order to protect health data against attacks such as side-channel attacks, physical attacks, and malicious attacks, as well as to maintain privacy and prevent unauthorised ingress to health data.
(2): Accuracy: Accuracy is essential for healthcare system carers. Depending on how the system is used, accuracy in healthcare IoT systems refers to how accurately the data collected reflect the patient’s condition, how accurate the data used in the computation were, and how accurately the decision was made.
(3): Performance: Healthcare providers must perform well in order to obtain precise data, process the data, and offer services quickly. This parameter is a combination of efficiency, load balancing, resource utilization, overhead, and computing time, as well as network quality of service (QoS) factors including throughput, latency, delivery rate, mean time between failures, and bandwidth usage.
(4): Time: The time it takes for consumers to receive service after making a request is described by this parameter. The concept of time encompasses calculation time, average response time, execution time (run-time), and latency (as the time lag in healthcare systems).
(5): Cost: This refers to the overall expense a patient who requests healthcare services may incur to receive the best care possible. The cost of computation, communication, data storage, and the upkeep of the desired service are all included.
(6): Energy: Energy conservation is crucial for device and network survival, since HIoT devices lack resources and are powered with meagre energy resources. Additionally, when there are more HIoT devices linked to the network, the network’s energy usage grows as well. On the other hand, more energy use results in higher operating expenses, more carbon dioxide produced, and a shorter network lifetime.
(7): Interoperability: Interoperability refers to the capability of more than two HIoT systems to communicate and exchange information in a dependable, consistent, and efficient manner, use the information exchanged, and share resources. More than one medical informatics system, for example, may be necessary. The capacity to successfully grasp data across organizational or system boundaries is referred to as data interoperability. HIoT systems require the usage of standardized communication as well as a number of other interoperability-supporting technologies.
(8): Scalability: This refers to the capability of the system to extend and build an IoT-based healthcare methodology as service needs and expectations grow. These capabilities can be leveraged to create smart devices, new operations that act as user service nodes, and network infrastructures, while not adjusting the quality or effectiveness of healthcare services. Extending the system requires either the addition of new hardware or services or improvement of the operation of current hardware or services.
(9): Reliability: The capacity of a system to carry out its necessary functions under pre-determined circumstances and at a predetermined time. A healthcare system based on IoT is said to be reliable if it can provide requested services to patients in most conditions.

3. Taxonomic Exploration of Healthcare IoT

IoT healthcare systems developed from clinic-based systems to customer-based systems were discussed by Farahani et al. [48]. A multi-layer IoT healthcare architecture with device, fog, and cloud layers has also been created to transform traditional healthcare systems into intuitive healthcare systems. Important products and services, on the other hand, were discussed, including mobile health, anomaly detection, early warning scores, ambient assisted living, and two real-world case studies involving smart eyewear for covert continuous heart rate monitoring and smart gloves in IoT for Parkinson’s disease. Some of the difficulties and constraints of this market are data management, scalability, security, privacy, interoperability, and standardization. However, it should be highlighted that this study was not carried out methodically, and did not take into account the years covered by the evaluated publications, the taxonomy, the articles chosen, or future research.

Cloud IoT-health, a paradigm for cloud computing with the IoT, was introduced by Darwish et al. [49]. Before introducing the IoT and cloud computing as brand-new technologies for use in healthcare systems, the authors of this paper dispensed a full overview of their histories and current applications in healthcare systems. Then, particular challenges and issues within this spectrum were identified, including standardization, storage, scalability, and adaptability. Despite offering sufficient arguments, this evaluation was not systematic, the process for selecting the papers was murky, no taxonomy was supplied for the chosen studies, and the analyzed publications’ covered years were not specified.

A study on advanced IoT that offers individualized healthcare solutions was presented by Qi et al. [50]. A four-layer design with levels for sensors, networks, data processing, and applications was developed for this inquiry, and the technology employed in each of these tiers was fully explained. The authors also discussed the challenges of encouraging researchers to conduct fresh research. The structuring of this survey, however, was lacking, and it was not evident how the papers were picked. The papers under consideration did not cover any certain years, and no taxonomy of the publications under study was dispensed.

Qi et al. developed their idea into four layers using an IoT layer-based method and proposed a physical activity recognition and monitoring architecture. Emerging tendencies were also described for researchers [51]. However, this review was not carried out methodically, despite the article’s title. Additionally, there was no special method of choosing the researched articles or any sort of classification to dispense readers with a clear picture. Additionally, articles published in 2019 and 2020 were not taken into account. Additionally, Dhanvijay and Patil dispensed a survey that examined the most significant recent technological advancements and how they may be used in IoT healthcare systems [52]. They focused particularly on WBAN and its security features.

In today’s real-time applications, QoS and quality of experience (QoE) play a vital role. HIoT service systems are related to various types of quality indicators which can be qualitative/quantitative, discrete/continuous, etc. There are recent studies that propose an overall model normalization for the adequate prediction and presentation of QoS/QoE in telecommunication systems that are used for better quality estimation. The overall normalization of the quality models is an important step to adequately estimate the quality in use, which cannot be assessed due to the differences between the various software products [53,54,55]. Silva et al. proposed a QoE model for providing context-aware electronic healthcare services. This model improved the user experience by producing better quality in the provision of healthcare services [56]. Narralla et al. surveyed QoE in mobile healthcare. They explored the role of 6G technology in order to enhance both QoS and QoE. They concluded that not only is QoS sufficient in healthcare, but QoE is also needed in the modern healthcare environment [57].

This entry is adapted from the peer-reviewed paper 10.3390/app132212135

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