AIoT and Big Data Analytics for Smart Healthcare Applications

The incorporation of semantics and the necessary interoperability within these aspects is essential for the domain's proper operation as well as execution. Healthcare systems have become an ideal arena of IoT because they tackle the problems of humanity, especially of an older population whilst providing secure and high-quality home care and support. The use of IoT technologies in healthcare will improve the quality of human life, chronic illness monitoring, hazard detection, and life-saving measures. To get more useful information from biomedical big data, it must have interoperability. In the latest times, an increasing count of organizations and businesses have expressed interest in combining semantic web technologies alongside healthcare big data to transform data into knowledge and understanding. Even though we can see a systematic acceptance of semantic technologies-based applications in the IoT domain and across the Internet, the cumulative actual implementations are insufficient to provide real-world rooted standards and guidelines to follow. This sets the stage for this work, which attempts to describe current developments in the application of semantic technologies in the IoT domain. This motivates the authors to examine and highlight some of the developing developments in semantic technology, its effects in the IoT area, and how they are together seen in the health-care. Over the last several times, there has been a lot of emphasis on using SWT to enhance the uptake of sensor networks, IoT, and WoT. Indeed, to tackle semantic interoperability and other issues in health care domains, there is a need to comprehend its means of construction.

In the present scenario, healthcare has made significant progress with the assistance of smart devices involving effective sensors and Internet of Things devices. In context to this, the combination of IoT and cloud architectures are rigorously exploited in order to process the large amount of data that would be generated by the wearable sensor networks in near real-time applications by making use of Artificial Intelligence supporting smart healthcare systems. In the current scenario of globalization, in addition to the increased facilities, a wide variety of other challenges are worked upon for providing quality and efficient healthcare benefits and facilities by making use of cost-effective instruments and world class technologies.

 An important factor for the physical and mental health of a human being, the performance throughout the day as well as safety is the sleep quality. Effective quality of sleep can help avoid the risk of mental depression and chronic diseases. Sleep promotes the brain to actively get associated with the activity that is being performed and helps in preventing various accidents that might be caused due to falling asleep. For the analysis of the sleep quality, a continuous monitoring system is necessary which generates effective results. With the aid of rapid improvisation of mobile and sensor technology as well as the emerging trends of Internet of things technology, there is a good opportunity of development of a reliable and effective sleep quality monitoring system. This chapter effectively describes the background and applicability of Internet of things for such systems involved in sleep monitoring. The study begins with the review of the quality of sleep, the importance related to the monitoring of sleep quality, the employability of Internet of things in this and its relevant field, as well as the open issues and challenges in this and its related fields.

The IoT technology supports the preamble which would promote a cost effective and consistent system in order to monitor the quality of sleep-in individuals. There are several existing systems for the same purpose which involves a large amount of cost and are cumbersome to implement. To overcome the same issue, the chapter narrates an inventive system for monitoring and analyzing sleep patterns by making use of effective parameters. In this domain, a combination of clinical medicine, bioengineering, neuroscience, epidemiology, mHealth, Computer Science, as well as Human Computer interactio,n in order to approach the challenge of digitization of sleep from a multidisciplinary perspective. This chapter describes the state of art technologies involved in sleep monitoring and discusses the challenges and opportunities involved from the initial step of acquiring the data to the applicability of the acquired data based on the consumer level and clinical settings.

Electronic gadgets, actuators, sensors, and software link every element of an active network. The Internet of Things is the name of this network (IoT). AI technology may help networks, sensors, and users create a large quantity of data by assisting in the collection of data and the development of applications. The combination of AI with IoT may advance fields including public safety, education, healthcare, energy, transportation, and other value-added services. Smart health care makes extensive use of the Internet of Things (IoT), notably in the areas of emergency services, intelligent computing, sensors, security, and remote monitoring. Data privacy, integrity, and freshness are just a few of the security issues that must be resolved in a smart hospital. Additionally, there are privacy risks for patients, data eavesdropping, data integrity, and unique identification. IoT technology may be used to monitor a patient's health as well as their data. A patient's status might be tracked remotely and in real time using the internet and other technologies. Additionally, it enables the early identification and treatment of diseases that pose a danger to life. Medical records may be gathered and statistical information on a patient's condition may be provided via an IoT-enabled gadget.

With the correct software, huge amounts of data may be handled quickly and without errors. As a consequence of these advancements, which strive to fulfil patients' unique requirements while simultaneously enhancing treatment effectiveness, modern medicine is on the verge of a renaissance. The essential technologies that underpin smart healthcare are briefly described, together with the successes and challenges they have faced, the current status of these technologies in important medical areas, and the possibilities for the future of smart healthcare. Sensors gather data, which is subsequently sent over the internet of things (IoT) to supercomputers and cloud computing for processing and analysis.

Sleep is one of the most important biological processes acknowledged as a vital determinant of human performance and health. Sleep has been acknowledged to promote healing, restore energy, improve the immune system through interactions, and affect human behaviour and brain functions. To this end, even the transient alteration of sleeping patterns, including severe sleep deprivation, can impair one's cognitive performance and judgment, even as prolonged aberrations have been associated with the development of disease. The existing global sleep trends indicate a decrement in average sleep durations. Owing to such trends and the various implications of sleep on human well-being and health, enhanced characterisation of the sleep attributes indicates a public health priority.

Further, the advancement and use of multi-modal sensors with technologies to monitor physical activity, sleep, and circadian rhythms have increased dramatically in recent years. For the first time, accurate sleep monitoring on a large scale is now possible. However, there is a need to overcome several significant challenges to realise the full potential of these technologies for individuals, medicine, and research. In this chapter, a review of the present levels of the sleep-monitoring technologies in patients with cognitive impairments, in addition to assessing the difficulties and potentials lying ahead, from data gathering through the ultimate execution of findings within the consumer and clinical contexts.. Further, the chapter will review the advantages and disadvantages of the extant and novel sensing technologies, focusing on new datadriven technologies that include Artificial Intelligence. 

IoT is intelligent sensors and actuators which assemble to form an IoT device. The algorithms employed make the system make up a wise decision. These systems can use artificial Intelligence algorithms to make intelligent decisions. The previous work employs devices that compute normal from abnormal heart rates. These devices are intelligent machines that are carried with the individual. They are also used to calculate the ECG of the personnel. This information understands the behavior of the personnel. The knowledge is sensed and passed to the devices using the Bluetooth technique. This data segment into healthy or unwell being sections. The processing amalgamates transformation, conversion w.r.t format, and section labeling. The iforest approach excludes the outliers from the data set. The suggestion improves the previous work by predicting the abnormality before in hand by 17.5%. Many lives can be saved, and will help improve their lives by adopting this method.

The patient's medical data is very valuable and sensitive. Hackers are always trying to steal or tamper with this patient's data. Cyber-criminals can misuse or sell this patient's data. Protecting medical data, and ensuring security and privacy of data is a statutory and ethical mandate for healthcare services providers. Implementation of blockchain technology enhances the security, confidentiality, and traceability of patient data. Secure sharing of patient data among all stakeholders of healthcare ecosystems ensures quality and high speed in services.
Objective: This paper's objective is to review data security applications in the healthcare domain using blockchain technology and present the highlights from selected survey papers. The second objective is to implement data security using a web-based prototype based on blockchain technology.
Method: This blockchain technology enables secure online sharing of data using open, distributed, denationalized, and immutable ledgers. Blockchain-based online transactions are taking place between sender and receiver directly. Blockchain-based transactions eliminate the need for third-party intermediate agents. Data. Authors applied blockchain technology for the implementation of oral health big data using a web portal. Results: The taxonomy of concepts applied in blockchain and EHR, Smart Contracts, and Healthcare 4.0 systems in healthcare from the literature are mapped. To identify research gaps, a comparative study of curated survey papers is conducted with specific variables. The results of web portal implementation are discussed.
Conclusion: At the out, the authors tried to provide a holistic view of blockchain applications and presented insight into systems design by discussing algorithms, techniques and methods applied. Authors conclude that intelligent systems integrated blockchain systems are going to play a vital role in the healthcare industry and enhance the quality and efficiency of services.

Recently, fake news has become a serious problem in our society majorly due to the cheap and easy availability of social media at every corner of the world. The widespread dissemination of false news has the potential to have a variety of harmful consequences for people and society. Hence, many researchers are finding different ways to detect fake news in a given news corpus. So here we came up with the idea of fake news detection using machine learning that detects fake news over the real news. During pandemic, fake news detection played an important role. Detection and identification of fake news in the social media, or any related news channels has played a major responsible sector to avoid unnecessary panic situation in mankind.

This paper is aimed at developing a Machine Learning model for deception detection using Natural Language processing techniques and machine learning algorithms. It detects fake news that comes from non-reputable sources which mislead people and distracts them with various fraud messages and unnecessary texts, by building a model using count vectorise, TF-IDF and logistic regression algorithm. Using this algorithm, the proposed technique identifies and rectifies real and fake news and this is an important sector during the pandemic situation.

However, there is difficulty in choosing the right metric for the evaluation of the model. Classification accuracy is one of the most used metrics to detect the performance of the model, in this paper we consider the parameters such as F1 score, confusion matrix, precision and recall. Abstract environment.

The future direction of modern medicine is toward “smart healthcare,” which incorporates a new generation of information technology to meet patient needs individually while increasing the effectiveness of medical care. This greatly improves the patient experience with medical and health services. Nowadays, due to people's lifestyles, diabetic retinopathy is one of the most serious health issues they confront. A deviation from the norm in which long-term diabetes affects the human retina is called diabetic retinopathy (DR). Diabetes is a chronic condition related to an expanding measure of glucose levels. As the degree of glucose builds, a few adjustments happen in the veins of the retina. Patients' vision may begin to deteriorate as their diabetes progresses, resulting in diabetic retinopathy. It is exceptionally far-reaching among moderately aged and older individuals. Thus there is a need to detect diabetic retinopathy at an early stage automatically. This study aims to build an intelligent framework that uses fundus images of the eye (retina) and performs image analysis to extract the features. Images are trained by the knowledge relegation approach, and the severity of the DR is classified using K-nearest neighbors. The proposed model achieved a test accuracy of 99%, 61%, 100%, 94%, and 88% for each of the five classes of diabetic retinopathy: proliferative diabetic retinopathy, no diabetic retinopathy, mild diabetic retinopathy, moderate diabetic retinopathy, and severe diabetic retinopathy.

A brain stroke is a disruption of blood circulation to the cerebrum. As per recent analysis, adult death and disability are primarily brought over by brain stroke. The World Health Organization (WHO), reports that the primary cause of death and property damage worldwide is brain stroke. Early detection of the signs and symptoms of a stroke can help to reduce risk factor of death by up to 50%. A stroke is more likely to occur in adults over the age of 55. An increasing number of people are experiencing this crippling and frequently fatal form of stroke, which results in cerebral hemorrhage. Various machine learning (ML) models were developed to predict the possibility that a brain stroke would occur. To predict the brain stroke, the proposed system used the CNN algorithm. The existing approaches are k-NN, Support Vector Machine (SVM), Genetic Algorithm (GA), Naïve Bayes classifier, J48 algorithm, Logistic Regression (LR) and Random Forest (RF). This requires more time to train the model and it is difficult to debug. And these are not suitable for large datasets. The proposed system makes predictions using CNN algorithm, a deep learning technique. It includes a multilayer perceptron for the prediction task and an autoencoder for eliminating and capturing non-linear correlations between parameters. The proposed system is contrasted with existing system and it shows an enhancement in the capability to anticipate the stroke. The proposed system achieved an accuracy of 89%.

The Electronic Health Record (EHR) is used for maintaining patients’ medical records in the hospital. The EHR contains the details of clinical related data of the patients under a particular provider. The EHR contains information on a patient's demographics, medications, previous medical history, laboratory results, and reports like X-rays. The EHR system is introduced in order to share the details with other health providers such as laboratories, pharmacies, emergency facilities, and clinics so that they will have all the medical history of the patient’s health conditions. These can be accessed from anywhere via any smart device. A single record for one patient across all departments. Sharing EHR with different health care providers is a major challenge since this health record is stored on centralized servers patients cannot share this information when required. To overcome this issue we have come up with an approach of using IPFS (Inter Planetary File Systems) to store this EHR in a decentralized manner and an RSA algorithm to encrypt the Health Record. By using the combination of blockchain and cryptography a secure platform can be developed for providing the patient with full control over their health record and also maintaining data integrity. 

This century has witness a substantial increase in elderly population. Health issues like depression and dementia are more prominent in these elderly populations which demand Assisted Living environment. The engagement of technology is seen as a solution for the Assisted Living environment. With the help of technology, Ambient Assisted Living (AAL) has become a field of research. As AAL strives to seamlessly connect information technology with people's daily lives, the buzz word called Internet of Things (IoT) exhibits significant promise for developing technical solutions in this field. Geo-fencing is one such location sensing tool that uses IOT and GPS for defining geographical boundaries and is used for putting e-fences to the needy people in their ambiances. This article aims to provide a safe tracker environment that allows the elderly people to continue with their daily activities. In this article, application built for elderly people is explained. Through this application, the person gets the direction to reach back home or the alert message is sent to the family member or the caretaker. The alert message is sent while the person is out of the fencing area to himself or to the care takers and the location of the person can be tracked. This app can also be modified for different users like person with disability, game like pub-G players etc. for the situations where the device sense that the person is in a danger zone or out of the geo-fencing. 

The vision of this device is to design and construct the blind-friendly embedded device. The blind and visually handicapped have difficulty utilizing mobile phones because social media and online banking programs on smartphones are difficult for them to utilize. For quick bank transactions, ATMs are used. If blind individuals use the ATM and it isn't designed with visually impaired persons in mind, there will be privacy concerns. Using mobile phones with the assistance of others may jeopardize their security and privacy. Touch screens were not designed with visually impaired persons in mind. They are uneasy using cell phones in public due to current technologies. When visually impaired persons walk, they use a stick, which can be replaced as well. By gaining access to all capabilities of smart phones, the developed system would assist visually impaired persons in making their lives much easier. 

Premature adolescents with Retinopathy of Prematurity (ROP), a fibrovascular proliferative condition, have difficulties with the maturing peripheral retinal vasculature. Early identification of ROP is achievable in stages 1 and 2, distinguished by a demarcation line and ridge that divides the peripheral retina from the vascularized retina. Because newborn retinal images have poor contrast, it is difficult to distinguish demarcation lines or ridges. This study used segmentation and convolutional neural networks to detect ridges, which are crucial landmarks in the diagnosis of ROP. Our contribution is implementing Mask R-CNN for identifying boundary line/ridge recognition, which enables doctors to identify ROP stage 2 more accurately. To combat poor image quality, the suggested approach uses a pre-processing stage of image augmentation. In this study, the utility of the Convolutional Neural Network was examined to localize ridges in labeled neonatal photos. The KIDROP study and a dataset comprising 220 photos of 45 infants were used. Using the segmentation of the ridge region as the ground truth, 175 retinal images were used to train the system. The system's detection accuracy was 0.94, with 45 images under test, proving that data augmentation detection in conjunction with image normalizing pre-processing allows accurate identification of the ROP in its early stages.

Soft robotics is one of the trending subdomains of robotics. It involves the application of compliant materials for building robotic mechanisms and controlling them using robot programming. This chapter discusses some of the recent applications of soft robotics in the medical field and their future scope. Minimal Invasive Surgeries (MIS) and Natural Orifice Transluminal Endoscopic Surgeries (NOTES) are the two commonly used surgeries, and the most familiar form of these two surgeries is endoscopy. In this chapter, we will discuss how soft robotics can be applied in both MIS and NOTES. This chapter will review the soft robotics applications in the medical field. This chapter will also discusses soft robotics's challenges and future directions in the healthcare industry.

Robotics began roughly 30 years ago in medical applications, but it is still relatively young for biological applications. Because of the precision, accuracy and reproducibility of robotic technology, robotic interventions in medical fields, such as robotic surgery, can enable doctors to work inside the human body, which is either non-invasive or minimally invasive, with improved surgical results. The importance of medical robots in the medical sector is intended to deliver good outcomes to assist people in doing complex tasks that need a significant amount of time, accuracy, concentration, and other routines that cannot be accomplished solely through human capability. Due to advancements in AI and IoT and their convergence to AIoT, the potential of medical robots has tremendously increased in the healthcare industry. The chapter outlines the various applications of robotics in the healthcare sector, including surgical, rehabilitation, telemedicine, and diagnostic. The advantages of robotics in Healthcare are highlighted, along with the discussion on the current and future challenges in their deployment and adoption. The role of AIoT in enhancing these healthcare robots' cognitive and other capabilities is also discussed. Finally, the future of robotics in Healthcare is explored, including emerging trends and technologies, their impact on the healthcare industry, and the potential for innovation and growth. 

Technology has changed almost all aspects of our life. Similarly, in the medical field, the new technology is Robotic surgery. Robotic surgery involves employing robots in the process of surgery. The employed robot, known generally as the surgical robot, is self-regulating, partially or entirely computer-controlled, and can be programmed as required for the surgery. As different surgical robots are employed for different types of surgery, robotic surgery improves patient care and ensures better treatment than regular surgery. The purpose of this article is to provide an outline of the main ideas of robotic applications used in surgery. This article aims to provide an overview of robotics's current and future applications in surgical operations and the advantages and disadvantages of surgical robots. 

The Internet of Things (IoT) is a quickly expanding environment which combines software, hardware, physical components, as well as computing tools for data collection, sharing, or rather interaction. The IoT enables a unified platform for humans to interact with a wide range of physical and virtual objects, like personalised healthcare domains. Due to the explosive growth and advancement of the internet, traditional patient care strategies have enhanced with the replacing e-medical records mechanisms. The use of IoT technology provides medical modern healthcare equipment device setting for both physicians and clients. IoT devices and Artificial Intelligence are beneficial in many implementations, starting with remote weather monitoring to mechanical mechanisation. Furthermore, medical care applications are showing a strong interest in IoT devices due to cost savings, easiness of using it, and an increase in service quality. The most recent services for IoT-based healthcare, which have been investigated and are still facing challenges in the clinical setting, are required for intellectual, creative solutions. An exploration of prospects for artificial intelligence and the internet of things in the medical sector is provided in this chapter.