E-healthcare Monitoring Research Articles

A secured and energy-efficient system for patient e-healthcare monitoring using the Internet of Medical Things (IoMT)

Introduction: The Internet of Things (IoT) is gaining popularity in several industries owing to the autonomous and low-cost functioning of its sensors. In medical and healthcare usage, IoT gadgets provide an environment to detect patients' medical problems, such as blood volume, oxygen concentration, pulse, temperatures, etc. and take emergency action as necessary. The problem of imbalanced energy usage across biosensor nodes slows down the transmission of patient data to distant centres and has a detrimental effect on the health industry. In addition, the patient's sensitive information is sent through the insecure Internet and is exposed to potential threats. For clinical uses, information privacy and stability against hostile traffic constitute a further research challenge. Methods: This article proposes a Secured and Energy-Efficient System (SEES-IoMT) e-healthcare utilizing the Internet of Medical Things (IoMT) monitoring, the main goal of which is to reduce the connectivity cost and energy usage between sensing devices while feasibly forwarding the medical data. SEES-IoMT also guarantees the clinical data of the patients against unverified and malevolent nodes to enhance the privacy and security of the system.Result and Discussion: In consideration of the memory and power limitations of healthcare IoT gadgets, this approach is designed to be very lightweight. A thorough examination of this system's safety is performed to demonstrate its reliability.Conclusion: In terms of computing speed and security, the research compares SEES-IoMT to relevant methods in the IoT medical environment to demonstrate its applicability and resilience.

A blockchain-based secured system using the Internet of Medical Things (IOMT) network for e-healthcare monitoring

The Internet of Things (IoT) has expanded because of the development of information technology. IoT impacts several fields, including the medical profession, smart cities, and data management. The development of sustainable medical systems is significantly aided by the Internet of Medical Things (IoMT). IoMT has a big impact on healthcare since it makes it easier to monitor and validate data about patients before storing it in the cloud. Keeping all data confidential and secured is essential since the IoMT is a big-data platform that is expanding quickly.This paper will apply this technical advancement to the realm of health, namely e-health. The research provides an IoMT-based monitoring tool for personal health surveillance. Block chain technology may resolve present interoperability issues in health-related systems and offer technological solutions that allow the electronic transfer of medical records to medical organizations and medical specialists. However, the transmission and security of people's health data remain a major concern in IoMT devices across a wide range of product categories. This research uses block chain as a secure solution to satisfy the requirements of medical privacy and data security. In this study, a Block chain-based IoMT security System (BC-IoMT-SS) is presented based on BC technology in IoMT for the security, privacy, and management of patient information. The suggested framework is successfully implemented to fulfill the optimal privacy and safety criteria for medical data management in IoMT gadgets. Utilizing the block chain's key, a healthcare application system has been created in which the person's health data may safely generate alerts for verified healthcare practitioners. Simulation results demonstrate that the suggested BC-IoMT-SS technique produces a high precision ratio of 94%, an efficiency ratio of 94%, a shorter delay of 0.63s, and a shorter reaction time compared to other current methods.

A Hyb-WGWO and Deep EMC–based Intelligent E-healthcare monitoring model for patient condition diagnosis in internet of things connected applications

A Hyb-WGWO and Deep EMC–based Intelligent E-healthcare monitoring model for patient condition diagnosis in internet of things connected applications

Dictionary Learning-Based Multichannel ECG Reconstruction Using Compressive Sensing

Multi-channel electrocardiogram (MECG) compression on lightweight wireless body area network (WBAN) is highly challenging for long-term eHealthcare monitoring. Energy consumption involved in wireless transmission poses an obstacle in the implementation of WBAN. MECG is widely used to diagnose cardiovascular diseases (CVDs), which require a considerable time to extract sufficient clinically relevant data from subjects. Since wireless MECG data transmission dominates energy cost and memory consumption of wireless sensor nodes, it is favorable to reduce the data size without any loss of diagnostic information. Compressed sensing (CS) provides efficient encoding schemes for data reduction and energy consumption in wireless transmission. We propose an adaptive multiple dictionary learning-based joint CS model for MECG compression, which exploits spatial correlation and adaptive features existing in MECG signals. We demonstrate performance of the proposed algorithm with the Physikalisch-Technische Bundesanstalt database (PTB) for MECG compression. Results from the proposed scheme can achieve excellent reconstruction quality with fewer measurements than other existing CS-based approaches. It is the most suitable technique for long-term MECG compression using lightweight WBAN devices.

Highly Conformal Polymers for Ambulatory Electrophysiological Sensing.

Stable ambulatory electrophysiological sensing is widely used for smart e-healthcare monitoring, clinical diagnosis of cardiovascular diseases, treatment of neurological diseases, and intelligent human-machine interaction. As the favorable signal interaction platform of electrophysiological sensing, the conformal property of on-skin electrodes is an extremely crucial factor that can affect the stability of long-term ambulatory electrophysiological sensing. From the perspective of materials, to realize conformal contact between electrodes and skin for stable sensing, highly conformal polymers are in great demand and attracting ever-growing attention. This review focuses on the recent progress of highly conformal polymers for ambulatory electrophysiological sensing, including their synthetic methods, conformal property, and potential applications. Specifically, three main types of highly conformal polymers for stable long-term electrophysiological signals monitoring are proposed, including nature silk fibroin based conformal polymers, marine mussels bioinspired conformal polymers, and other conformal polymers such as zwitterionic polymers and polyacrylamides. Furthermore, the future challenges and opportunities in preparing highly conformal polymers for on-skin electrodes are also highlighted.

High-resolution flexible electronic devices by electrohydrodynamic jet printing: From materials toward applications

High-resolution flexible electronic devices are widely used in the fields of soft robotics, smart human-machine interaction, and intelligent e-healthcare monitoring due to their mechanical flexibility, ductility, and compactness. The electrohydrodynamic jet printing (e-jet printing) technique is used for constructing high-resolution and cross-scale flexible electronic devices such as field-effect transistors (FETs), flexible sensors, and flexible displays. As a result, researchers are paying close attention to e-jet printing flexible electronic devices. In this review, we focused on the latest advancements in high-resolution flexible electronics made by e-jet printing technology, including various materials used in e-jet printing inks, the process control of e-jet printing, and their applications. First, we summarized various functional ink materials available for e-jet printing, including organic, inorganic, and hybrid materials. Then, the interface controlling the progress of e-jet printing was discussed in detail, including the physical and chemical properties of the functional ink, the interfacial wettability between the ink and substrate, and the micro-droplet injection behavior in a high-voltage field. Additionally, various applications of e-jet printing in the fields of flexible electrodes, FETs, flexible sensors, and flexible displays were demonstrated. Finally, the future problems and potential associated with the development of next generation e-jet printing technology for flexible electronic devices were also presented.

An efficient mutual authentication and privacy prevention scheme for e-healthcare monitoring

The progressive development in online healthcare monitoring may facilitate better service for recovered patients from some pandemic diseases like the novel Covid-19 and even in well-known diseases such as cancer, heart attack, and many more. This paper brings a mutual authentication protocol for the e-healthcare monitoring system using the telecare medical information system with body sensors. This scheme comes with a secure platform for communication by using three phases: patient data upload phase, treatment phase, and report delivery phase. The patient’s medical information is susceptible and must be protected from any modification. The two security issues (secure communication and privacy of patient information) are essential for the transmission over the public channel. The proposed protocol uses mobile characteristics that allow the recovered patients to use medical facilities effectively. The well-known traditional informal security analysis like the Man-in-the-middle attack, patient anonymity, doctor anonymity, and many more are validated to judge the security aspect of the proposed protocol. In addition, the widely accepted formal security analysis (both Burrows–Abadi–Needham (BAN) logic and Real-or-Random Model (ROR)) are investigated for the session-key security. Finally, the proposed e-healthcare monitoring protocol provides an efficient characteristic in terms of communication, computation, and storage cost compared to existing literature.

Photovoltaic and triboelectrification empowered light-weight flexible self-charging asymmetric supercapacitor cell for self-powered multifunctional electronics

Herein, we highlight the simplistic fabrication of a light-weight, flexible self-charging power pack by the prudent integration of two paper-based high-performance triboelectric nanogenerators (HPTENGs), one commercial semi-flexible photovoltaic/solar cell and a paper-based all-solid-state asymmetric-type supercapacitor (ASSASC) with optimized performance. Each HPTENG unit comprises of a ~20 wt% barium titanate nanoparticles loaded surface micropatterned post-polled PDMS composite (PDMS–20BTO) film-strip impregnated with graphite coated Whatman® 41 filter paper (GFP) as negative and a polypyrrole electrodeposited GFP as positive triboelectric friction layers, respectively. Contrariwise, the ASSASC consists of a nickel-cobalt-molybdenum oxide–graphitic C3N4 hybrid composite coated GFP (NCMO–gCN2/GFP) as positive and a graphitic C3N4 modified reduced graphene oxide coated GFP (gCN–m–RGO/GFP) as negative electrodes, separated by a thin PVA–KOH gel-type electrolyte membrane. Finally, under deformations/stress and illumination of solar light individually on top of our photovoltaic and HPTENGs driven self-charging ASSASC (PTSCASC) power pack, it efficaciously generates electrical energy and consequently stores this generated energy as electrochemical energy for sustainable power supply. Our self-powered PTSCASC# power pack prototype (with two ASSASCs) has been effectually integrated with a medical smart patch for electric pulsatile mediated controlled drug release. Hence, our as-designed self-charging power pack possesses immense potentials for self-powered multifunctional electronics and smart e-healthcare monitoring systems.

Analysis on E Healthcare Monitoring System with Iot and Big Patient Data

Internet of Things (IoT) technology helped the development of healthcare from face-to-face consulting to the telemedicine. Smart healthcare system in IoT environment monitored the patient basic health signs such as heart rate, body temperature, and hospital room condition in real-time applications. The IoT and big data is an important challenge in many fields including smart healthcare systems due to its significance. Big data is employed to analyse the huge volume of data. Big data are significantly used in healthcare technique to determine the normal and abnormal patient condition. The doctors are easily analysed the patient condition in a short time. This system is very easy to design and use. It is employed to enhance the present healthcare system which preserves the lot of lives from death. Healthcare monitoring system in hospitals has experienced large development and portable healthcare monitoring systems with new technologies. Connected healthcare is an essential solution for hospital to record and analyse the patient data and to save money. The clustering and classification methods are used in existing methods. The clustering method is employed to group the similar data. The classification method is utilized to classify the patient data. A lot of healthcare technique was introduced by many researchers ranging from diagnosis to treatment and prevention on efficient e-health monitoring system. But, the accuracy level was not improved and time consumption was not reduced by existing techniques. In order to address these problems, different methods and techniques were reviewed for performing the e-healthcare monitoring system with big data. The machine learning techniques are used for efficient diseased patient health monitoring through the effective performance of feature selection, clustering and patient classification with increase the accuracy and minimum time consumption. The results are is performed using on different factors such as clustering accuracy, clustering time, classification accuracy, classification time, and error rate with respect to number of patient data.

A Secure IoT-Based Modern Healthcare System With Fault-Tolerant Decision Making Process

The advent of Internet of Things (IoT) has escalated the information sharing among various smart devices by many folds, irrespective of their geographical locations. Recently, applications like e-healthcare monitoring has attracted wide attention from the research community, where both the security and the effectiveness of the system are greatly imperative. However, to the best of our knowledge none of the existing literature can accomplish both these objectives (e.g., existing systems are not secure against physical attacks). This paper addresses the shortcomings in existing IoT-based healthcare system. We propose an enhanced system by introducing a Physical Unclonable Function (PUF)-based authentication scheme and a data driven fault-tolerant decision-making scheme for designing an IoT-based modern healthcare system. Analyses show that our proposed scheme is more secure and efficient than existing systems. Hence, it will be useful in designing an advanced IoT-based healthcare system.

E-healthcare monitoring using internet of things

Internet of Things has given a lot simpler answer for remote continuous wellbeing checking of patients from the clinic just as home. Sensors secure the information of different parameters in regards to patients’ wellbeing, and the Internet of Things stores that information and shows through the site, which gives access to remote observing. Utilization of Sensor decreases the human mistake, and the span of the framework lessens the involved space of the room. The extraordinary piece of this proposed arrangement is the alert age to give the recommended prescription to the patient in time. The other gainful zone of the framework is the plan of sending the warning through email and SMS alert if any of the wellbeing parameters crosses the limit esteem. Notice plan will keep the separate expert aware of the circumstance. Another huge region of the proposed arrangement is to make the ideal surroundings according to the necessity of patient’s wellbeing condition. In this paper, we have talked about the checking of pulse, circulatory strain, breath rate, body temperature, body development and saline dimensions.

A Magneto-Mechano-Electric Generator Based on Lead-Free Single-Crystal Fibers for Robust Scavenging of Ambient Magnetic Energy

The development of flexible, durable, and biocompatible multi-functional energy harvesters with exceptional power density is a challenging task for scavenging electricity from readily available energy resources to power e-healthcare monitoring devices and real smart electronics. Using flexible and biocompatible lead-free BZT-BCT single-crystal fibers, herein, we developed a magneto-mechano-electric (MME) generator with promising performance to harvest ambient magnetic energy with high robustness. Our optimized lead-free MME generator can produce an open-circuit voltage of 11 V and a short-circuit current of 62 μA with a low alternating current magnetic field of 1 mT (≡10 Oe; resonant frequency = 60 Hz). After voltage rectification, a maximum DC power density of 504 μW-cm−3 was generated by this energy harvester, which was more than two to three orders of magnitude higher than the power density generated by a previously reported lead-free BaTiO3 nano-generator (0.84 µW-cm−3)1 and was comparable to that of a hazardous lead-based PZT/Ni generator (270 μW-cm−3)2. The harvested power was large enough to light a light-emitting diode (LED). The proposed lead-free MME generator was demonstrated to scavenge ambient magnetic energy and could be potentially applied in eco-friendly self-powered systems.

Internet of Things Utilization for eHealthcare Monitoring

Internet of Things Utilization for eHealthcare Monitoring

PFCBAS: Pairing Free and Provable Certificate-Based Aggregate Signature Scheme for the e-Healthcare Monitoring System

Recently, one of the most popular technologies of the modern era, the Internet of Things, allows the deployment and usage of various real-time test beds in various smart applications. One such application is the e-healthcare, in which patients’ healthcare related data are transmitted to the nearest base station and then to a local or remote server as per the requirements. The data related to patients’ health are sensitive and need special protection, therefore, the integrity and authentication of the sources of data generation are paramount concerns. However, several authentication or signature schemes that have been introduced in the past for this purpose are ID-based or having certificate-less settings. In these settings, a central authority, known as a trusted authority (TA), creates and distributes the secret key of every user. Thus, knowing the secrete key by the TA is called key escrow problem. But, these proposed schemes suffer from key distribution problems, which limit their applications in various applications. To mitigate these issues, this paper presents a certificate-based pairing free aggregate signature scheme (CBPFAS). The proposed scheme uses the merits of public key cryptography (PKC) and identity-based PKC (IDBPKC). The scheme is proven to be unforgeable, assuming the hardness of elliptic curve discrete log problem (ECDLP). The performance analysis shows that the proposed CBPFAS scheme executes in $0.78(n+1)$ ms in comparison to $9.63+1.17$ n ms in [1] , $9.63+0.78$ n ms in [2] , $9.63+3.39$ n ms in [3] , and $9.63+1.17$ n ms in [4] . From these results, it is concluded that the proposed pairing free certificate-based aggregate signature scheme performs better than its counterparts.

Designing high energy conversion efficient bio-inspired vitamin assisted single-structured based self-powered piezoelectric/wind/acoustic multi-energy harvester with remarkable power density

Development of flexible, durable and biocompatible piezoelectric/acoustic/wind multi-functional energy harvester with remarkable energy conversion efficiency, high current, and power density is of great challenge in scavenging electricity from multi-modes of energy sources for powering real smart electronics and e-healthcare monitoring devices. Here, bio-inspired vitamin B2 (VB2) assisted PVDF based multifunctional advanced energy material with high out-of-plane piezoelectric co-efficient of ~ –50.3 pC/N, is reported first time for harvesting huge electrical energy from multi-modes of alternative energy sources in a single device. Thus, an entirely organic based fully biocompatible piezoelectric nanogenerator (PNG) having multifunctional energy harvesting capabilities at a time (mechanical/acoustic/wind) has been designed by integrating VB2 with PVDF. VB2 is introduced in energy-harvesting family for first time as an effective β-phase (~93%) stabilizer for PVDF and would be better replacement of non-toxic expensive stabilizers. The fabricated nanogenerator reveals high output current (≈12.2 μA) and voltage (≈61.5 V), theoretically verified with finite element method (FEM) based simulation. The device with fast-charging capability shows remarkable peak power density of ≈9.3 mW/cm3 and ultrahigh energy conversion efficiency (≈62%) enabling lightening more than 100 LEDs directly and can power up a CD motor/mobile through capacitor charging. Moreover, generation of electricity from various noises allows potentiality of the device as an artificial acoustic ultra-sensor. Thus, it would provide more opportunities towards next generation multifunctional emerging power sources and in e-healthcare monitoring.

Bandwidth efficient designated verifier proxy signature scheme for healthcare wireless sensor networks

Recently, big data collection of e-healthcare monitoring using wireless sensor networks (WSN) have become common in practice. These WSN collect the data such as blood pressure, pH-value, pulse rate, etc. from a remote location based patient and then send to hospital/medical server. Since, the e-healthcare data is associated to a patient and thus, the confidentiality and authentication of data are critical issues. This article introduces a provably secure message recovery designated verifier proxy signature (MRDVPS) scheme to eliminate the issues. The proposed MRDVPS scheme is proven existential unforgeable (EUF) in the random oracle model (ROM), under the intractability of computational Diffie–Hellman (CDH) problem. Efficiency comparison shows that the scheme is the most appealing towards healthcare wireless sensor networks (HWSN).

Provably Secure Message Recovery Proxy Signature Scheme for Wireless Sensor Networks in e-Healthcare

Recently, the communication of healthcare monitoring data through wireless sensor networks has become a common practice. However, the authenticity, integrity and confidentiality of communicated data are great challenges. To solve the challenges, this article presents an efficient and provably secure proxy signature scheme with message recovery from pairings. The scheme is proven secure under adaptively chosen message attack in the random oracle model. Due to message recovery, the length of communicated signature is very short and thus our scheme is the best choice to deploy in WSN in e-healthcare monitoring systems. We prove the efficiency in terms of bandwidth required and computational cost.

Chronic adolescent stress alters adult regulation of the glucocorticoid receptor

Recently, big data collection of e-healthcare monitoring using wireless sensor networks (WSN) have become common in practice. These WSN collect the data such as blood pressure, pH-value, pulse rate, etc. from a remote location based patient and then send to hospital/medical server. Since, the e-healthcare data is associated to a patient and thus, the confidentiality and authentication of data are critical issues. This article introduces a provably secure message recovery designated verifier proxy signature (MRDVPS) scheme to eliminate the issues. The proposed MRDVPS scheme is proven existential unforgeable (EUF) in the random oracle model (ROM), under the intractability of computational Diffie–Hellman (CDH) problem. Efficiency comparison shows that the scheme is the most appealing towards healthcare wireless sensor networks (HWSN).

Cloud support for large scale e-healthcare systems

Rapid development of wearable devices and mobile cloud computing technologies has led to new opportunities for large scale e-healthcare systems. In these systems, individuals’ health information are remotely detected using wearable sensors and forwarded through wireless devices to a dedicated computing system for processing and evaluation where a set of specialists namely, hospitals, healthcare agencies and physicians will take care of such health information. Real-time or semi-real time health information are used for online monitoring of patients at home. This in fact enables the doctors and specialists to provide immediate medical treatments. Large scale e-healthcare systems aim at extending the monitoring coverage from individuals to include a crowd of people who live in communities, cities, or even up to a whole country. In this paper, we propose a large scale e-healthcare monitoring system that targets a crowd of individuals in a wide geographical area. The system is efficiently integrating many emerging technologies such as mobile computing, edge computing, wearable sensors, cloud computing, big data techniques, and decision support systems. It can offer remote monitoring of patients anytime and anywhere in a timely manner. The system also features some unique functions that are of great importance for patients’ health as well as for societies, cities, and countries. These unique features are characterized by taking long-term, proactive, and intelligent decisions for expected risks that might arise by detecting abnormal health patterns shown after analyzing huge amounts of patients’ data. Furthermore, it is using a set of supportive information to enhance the decision support system outcome. A rigorous set of evaluation experiments are conducted and presented to validate the efficiency of the proposed model. The obtained results show that the proposed model is scalable by handling a large number of monitored individuals with minimal overhead. Moreover, exploiting the cloud-based system reduces both the resources consumption and the delay overhead for each individual patient.

Security and Privacy in E-Healthcare Monitoring with WBAN: A Critical Review

In the current scenario Wireless Body Area network (WBAN) has made its prominent place among technological advancements to improve human health. Since WBAN uses a wireless technique for communication, it has various positive & striking features such as its unattended nature, unobtrusiveness, mobility and cost effectiveness. These WBANs are extremely essential for people with severe diseases. For example, Heart patients, pregnant women, mentally challenged people, etc. need a continuous observation. Thus, WBAN, on the one hand, is working as a virtual safeguard for its users. WBAN, on the other hand, exploits wireless media during its realization. All the constraints specially related to insecurity (openness) have also been elaborated here. Since health related information is tremendously crucial and confidential and also liable to the patient’s life. Therefore, we need to take care of mechanisms applied to WBANs to overcome all the issues and drawbacks related to security and privacy. This paper focuses on various limitations and their possible solutions available within WBANs in order to provide secure and private information management to its dependents and users.