The impact of the Internet of Things (IoT) on the advancement of the healthcare industry is immense. The ushering of the Medicine 4.0 has resulted in an increased effort to develop platforms, both at the hardware level as well as the underlying software level. This vision has led to the development of Healthcare IoT (H-IoT) systems. The basic enabling technologies include the communication systems between the sensing nodes and the processors; and the processing algorithms for generating an output from the data collected by the sensors. However, at present, these enabling technologies are also supported by several new technologies. The use of Artificial Intelligence (AI) has transformed the H-IoT systems at almost every level. The fog/edge paradigm is bringing the computing power close to the deployed network and hence mitigating many challenges in the process. While the big data allows handling an enormous amount of data. Additionally, the Software Defined Networks (SDNs) bring flexibility to the system while the blockchains are finding the most novel use cases in H-IoT systems. The Internet of Nano Things (IoNT) and Tactile Internet (TI) are driving the innovation in the H-IoT applications. This paper delves into the ways these technologies are transforming the H-IoT systems and also identifies the future course for improving the Quality of Service (QoS) using these new technologies.

The Future of Healthcare Internet of Things: A Survey of Emerging Technologies

The widespread adoption of the Internet of Things (IoT) technologies has drastically increased the breadth and depth of attack surfaces in networked systems, providing new mechanisms for the intrusion. In the context of smart-world critical infrastructures and cyber-physical systems, the rapid adoption of the IoT systems and infrastructures without thorough consideration for the risks and vulnerabilities has the potential for catastrophic damage to the privacy, safety, and security of individuals and corporations. While the IoT systems have the potential to increase productivity, accountability, traceability, and efficiency, their potential weaknesses are also more abundant. In this paper, we provide critical consideration of the security of the IoT systems as applied to smart-world critical infrastructures. Particularly, we carry out a detailed assessment of vulnerabilities in IoT-based critical infrastructures from the perspectives of applications, networking, operating systems, software, firmware, and hardware. In addition, we highlight the three key critical infrastructure IoT-based cyber-physical systems, namely the smart transportation, smart manufacturing, and smart grid. Moreover, we provide a broad collection of attack examples upon each of the key applications. Furthermore, we introduce a case study, in which we assess the impacts of potential attacks on critical IoT-based systems, using the smart transportation system as an example. Finally, we provide a set of best practices and address the necessary steps to enact countermeasures for any generic IoT-based critical infrastructure system.

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Secure Internet of Things (IoT)-Based Smart-World Critical Infrastructures: Survey, Case Study and Research Opportunities

5G-enabled ultra-sensitive fluorescence sensor for proactive prognosis of COVID-19.

Advances in flexible and stretchable electronics, functional nanomaterials, and micro/nano manufacturing have been made in recent years. These advances have accelerated the development of wearable sensors. Wearable sensors, with excellent flexibility, stretchability, durability, and sensitivity, have attractive application prospects in the next generation of personal devices for chronic disease care. Flexible and stretchable wearable sensors play an important role in endowing chronic disease care systems with the capability of long-term and real-time tracking of biomedical signals. These signals are closely associated with human body chronic conditions, such as heart rate, wrist/neck pulse, blood pressure, body temperature, and biofluids information. Monitoring these signals with wearable sensors provides a convenient and non-invasive way for chronic disease diagnoses and health monitoring. In this review, the applications of wearable sensors in chronic disease care are introduced. In addition, this review exploits a comprehensive investigation of requirements for flexibility and stretchability, and methods of nano-based enhancement. Furthermore, recent progress in wearable sensors—including pressure, strain, electrophysiological, electrochemical, temperature, and multifunctional sensors—is presented. Finally, opening research challenges and future directions of flexible and stretchable sensors are discussed.

Non-Invasive Flexible and Stretchable Wearable Sensors With Nano-Based Enhancement for Chronic Disease Care

Improved instantneous frequency estimation using an adaptive short-time fourier transform

ECG signals represent the electrical activity produced by the heart and play a very important role in the monitoring of cardiac diseases. As ECG provides us with information related to the functioning of the heart, hence it becomes much important in the field of medical diagnosis. But the problem with the recording of ECG due to which the information contained can be lost or change is the presence of power line interference in the ECG signal. Power line interference (PLI) means the presence of high-frequency noise in the ECG signal. In this paper we have presented the study that how ECG waveform is related to the heart functioning, and the sources of the noise associated with ECG recording especially we have discussed the high-frequency noise that is basically power line interference, and further we have developed an algorithm for the removal of these power line interferences caused by high-frequency noise contained in the ECG signals. The algorithm discussed in this paper is developed upon the principal of the moving average filter (MAF). In the process of removing high frequency we have performed the filtering of ECG signals and further, for keeping the ECG signals smooth, we have applied the polynomial curve fitting algorithm on the ECG signals. Development of the algorithm and de-noising of the signals is performed in the MATLAB and also a Simulink model is developed. The benefit of the proposed algorithm is the removing the necessity of the pre-processing steps of the ECG signals during the capturing of the raw signals. Therefore our algorithm can make the overall architecture of the ECG acquisition system simpler and cheaper, and also the computational cost is removed due to the removal of pre-processing steps.

Study of ECG Waveform and Development of an Algorithm for Removal of Power Line Interference

Now a day’s people are suffering from various diseases, as these diseases increase there is a need of diagnosis.
People, affected from specific disease need continuous monitoring and detection of the different parameter which are
affected by that disease. This problem can be solved by an autonomous embedded system, this system has the ability of
continuous monitoring of patient’s health. The patient can be observed or monitored from everywhere and also the data
can be sent to the health care centers where doctors and experts can keep the track of it. By this the importance of
embedded system increases in the medical world. This system uses both invasive as well as non-invasive sensors for
sensing. The specialized computing and monitoring devices are used for process, transport, and archive and visualization
of the acquired data. This idea can be applied for sportsmen, rescuers etc, the type of professions where the continuous
health monitoring is required. The embedded system application for e-health care shows dissimilar needs and their
characteristic, constitute problems at the computing and communication levels. This design proposes the widespread
network based stand that is followed by a distributed approach to combine the embedded system for e-health system. This
field uses an autonomous communication module, and determined by personal digital assistants (PDAs) to act as the
Master and to interconnect an embedded system to the computer networks. The information is being sent to the main
server that take care of the database and provides an access to the database. There is a use of cryptographic algorithms
and the protocols in all systems so that data can be secured.

https://www.myresearchjournals.com/index.php/SHRISTI/article/download/13120/12331

An Approach: e-Health System Design

This research paper presents a smart approach for design and implementation of a wearable system that is able to monitor the electrical activity of the heart of a person using a smartphone. The real-time ECG monitoring system is implemented using a FPGA IC, a smartphone, and a front-end amplifier circuit for amplifying the ECG signal, and other component includes a capacitor, comparator, and a register. A USB link of PHY/LINK 1.1 and delta modulator is implemented in the digital circuit block of the FPGA IC. The digital block for the delta modulator consist of a sinc filter, a sampler basically a D flip-flop, and a FIR filter. Clock frequency at which sampler works is 6 MHz, delta modulator takes the input voltage in the 0.25–1.25 V @ up to 1.75 kHz frequencies, and it generates a corresponding 8-bit code @ 23.5 kS/s. The FPGA IC and all other circuits are powered by a smartphone through a micro USB link and provide a power supply of DC 5 V. In our system, we have used the screen of the smartphone as a display device and the captured ECG is displayed on the smartphone. For the calculation of the heart rate of the captured ECG signal, we used the smartphone and the result with waveform is stored in the memory of smartphone.

Design and Implementation of a Wearable Real-Time ECG Monitoring System Based on Smartphone

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Raghuvendra Pratap Tripathi

Papers

Study of ECG Waveform and Development of an Algorithm for Removal of Power Line Interference

An Approach: e-Health System Design

Design and Implementation of a Wearable Real-Time ECG Monitoring System Based on Smartphone

Design and Implementation of a Smart System for Assistance of Sleepy Driver Using ECG EEG and other Physiological Signals

Efficient Multiplier-less Perceptron Architecture for Realization of Multilayer Perceptron Inference Models