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

As we know that developments in technology are introducing various methods for Tele-cardiology. Tele-cardiology includes many of the applications and this is one of the fields in telemedicine which have seen excellent growth. In the procedures of Tele-cardiology we record a extremely large amount of ECG real time data. Therefore we require an efficient and lossless technique that is able to perform compression of recorded ECG signals. In this paper we have studied and analyzed various lossless data compression techniques used in the compression of ECG signals. In the course of studying various techniques we have presented the analysis of some most widely used time domain techniques those are AZTEC (Amplitude zone time epoch coding) technique and Turning point technique (TP) and in transformation based compression techniques we have presented the study of Discrete Cosine Transform technique (DCT) performed with Huffman coding technique and Empirical Mode Decomposition (EMD) technique. The overall performance of all these techniques are studied and analyzed on the basis of two main parameters those are the compression ratio (CR) and Percent Root means square Difference (PRD). We have used the data base of physionet.org website for the calculation of CR and PRD. We have calculated and compared the CR and PRD values using all above discussed techniques for 28 sets of the recorded data.

Study of various data compression techniques used in lossless compression of ECG signals

In this research article we have explained the design and fabrication process of ECG patch electrodes that are highly stretchable, flexible and almost similar to skin these electrodes are going to be used in the long term and unconscious sensing of electrical activity of heart. The overall design and fabrication process is highly motivated by the developments of the nanotechnology. As the result of these developments in nanotechnology we are able to reduce the size, cost, and power requirements of the electrodes used in the sensing of various bio signal, in our design we have used the Carbon NanoTube (CNT) / PolyDiMethylSiloxane (PDMS) composite material for the fabrication of patch electrodes. These electrodes provides better performance over conventional electrodes and one more important thing is that these microelectrodes are waterproof and hence patient can also be monitored at the time of showering.

Design and fabrication of a nano patch electrode for ECG using CNT/PDMS

The risk of encountering new diseases is on the rise in medical centers globally. By employing advancements in medical sensors technology, new health monitoring programs are being developed for continuous monitoring of physiological parameters in patients. Since the stored medical data is personal health record of an individual, it requires delicate and secure handling. In wireless transmission networks, medical data is disposed of to avoid loss due to alteration, eavesdropping, etc. Hence, privacy and security of the medical data are the major considerations during wireless transfer through Medical Sensor Network of MSNs. This chapter delves upon understanding the working of a secure monitoring system wherein the data could be continuously observed with the support of MSNs. Process of sanctioning secure data to authorized users such as physician, clinician, or patient through the key provided to access the file are also explained. Comparative analysis of the encryption techniques such as paillier, RSA, and ELGamal has been included to make the reader aware in selecting a useful technique for a particular hospital application.

Advancements in Data Security and Privacy Techniques Used in IoT-Based Hospital Applications

In the presented paper we have explained the design and implementation of a system that is capable in monitoring of mental stress in real time. To determine the stress of the person we have tried to estimate the autonomic state of the person, the idea is based on the basic fundamental, and it states emotional arousal is directly linked with the autonomic state of the person. To determine the autonomic nervous system (ANS) activity we have taken the help of the some physiological signals that are in the control of ANS. We have monitored the Heart Rate Variability (HRV), Galvanic Skin Response (GSR), and respiration rate (RR) of the person to detect the mental stress. To perform the analysis of the above stated signals we have used software called Matlab. In this system we have created a Body Sensor Network (BSN) to acquire the raw physiological signals through a data acquisition card (DAC), we have used the Xbee module to create the BSN. After the acquisition and reception these signals are processed in Matlab Software. In Matlab we have created an algorithm using this algorithm our system differentiates the relaxed state with stress state. As we have designed this system for the monitoring the patients those are facing the issue of the panic attacks hence we have designed a separate module in this system which sends the message on the pre provided mobile number. In the message it sends the State of the person with the location of the person. To determine the location we have used the GPS module and the information is sent through a GSM module and a SIM.

Design and implementation of a real time stress monitoring system with the help of ECG using Matlab tool

The diagnosis of Cardio-Vascular diseases (CVD) is highly dependent on analysis of ECG signals. ECG analysis can be helpful in estimating the underlying cause and condition of heart in cardiac abnormality. The effectiveness of ECG signal analysis in detection of CVDs is widely accepted by professional healthcare service provider. Many algorithms have been proposed but almost all of them have some kind of limitations, and these limitations largely influence the effectiveness of ECG analysis. The performed research work is dedicated for design of unique self-organizing maps (SOMs) based neural network for classification of arrhythmia according to a particular ECG signal, the generation of SOMs is based on the certain unique signatures of ECG signals and have potential to classify different cardiac conditions. For extraction of unique features from ECG signals, we have proposed to use Fast Discrete Stockwell Transform (FDST). Since the proposed technique is a result of combining two different techniques hence called as hybrid technology. The purpose of using FDST is to identify unique signatures of ECG signals in a more improved manner than existing one, the term improved is used because it has several advantages over existing techniques such as wavelet and Fourier Transform based methods. Results obtained from the implementation of the technique are capable in visualizing the ECG sinus rhythm and arrhythmia conditions in form of unique SOM for each associated arrhythmia condition. This unique SOM based classification makes them ideal for being used as a diagnostic tool. This ability of arrhythmia classification using FDST and SOMs makes the technique unique and useful providing valuable information about patient condition. Using proposed technology a portable diagnosis tool for monitoring of patient at their site may be facilitated later, that will improve the quality of life of the patient by diagnosing cardiac condition.

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Raghuvendra Pratap Tripathi

Papers

Study of various data compression techniques used in lossless compression of ECG signals

Design and fabrication of a nano patch electrode for ECG using CNT/PDMS

Advancements in Data Security and Privacy Techniques Used in IoT-Based Hospital Applications

Design and implementation of a real time stress monitoring system with the help of ECG using Matlab tool

Classification of Cardiac Arrhythmia using Hybrid Technology of Fast Discrete Stockwell-Transform (FDST) and Self Organising Map