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The recent developments in the wireless body area networks (WBAN) play a vital role in the modern remote healthcare monitoring system. In WBAN, the deployed intelligent, resource-limited body sensors will collect the patients' biological information (BI) and communicate the same to the doctor for further action through the internet. But, during the communication among the WBAN entities, the privacy, security of the BI, and user's personal data need to be protected against various security threats. To address these security shortcomings, we proposed a computationally efficient privacy-preserving anonymous authentication scheme for resource-limited WBAN. Also, it preserves the physical security of sensors, the confidentiality of BI, and provides conditional privacy to the WBAN users. The comprehensive security and performance analysis section ensure that the proposed scheme is secure and efficient in terms of computation and communication overhead compared to that of other existing schemes.

Introduction: The future of healthcare systems relies on faster communication between sensors and smart devices, which can assist in taking decisions for betterment of patients. The responsiveness of wireless health care system in case of medical urgencies is crucial factor for survival of patient’s life. The routing protocol of infrastructure less based are playing key role in sending the information in such medical related emergencies. Therefore, it is important to study and identify the best routing protocol in wireless healthcare based system using Mobile Ad-hoc networks (MANETS). Methods: MANET routing protocols such as Ad-hoc On Demand Vector (AODV), Secure AODV (SAODV) and Hybrid Wireless Mesh Protocol (HWMP) through their routing environment are simulated through this research study. It is essential to highlight the effect of various attacks affecting the routing methodology of these protocols. Since Distributed Denial of Service (DDoS) attack is popularly talked about and is most vulnerable attack present in MANET environment, so this research study analyses AODV, SAODV and HWMP under DDoS attacks through various simulation parameters. Results: In this research study, most suitable routing protocols to handle DDoS attacks are simulated and estimated in terms of delay and packet delivery ratio in the scenario of changing nodes. This aids in providing implications to enhance existing protocols and alleviate the consequences of DDoS investigation by such attacks. Discussion: For ensuring the optimized routing, privacy and security of patient’s data during transmission in healthcare sector, MANET could be used as one of the important technology combined with IoT. For dealing with end to end data transmission of patient's sensitive data, MANET protocol plays a vital role in sending the information securely. Conclusion: The performance of AODV, SAODV and HWMP (Hybrid Wireless Mesh Protocol) are compared and tabularized which are the most popularly utilized protocols in healthcare environment. The simulation results shows that the HWMP out performed than AODV and SAODV routing protocol, in terms of evaluation metrics namely end-to-end delay and Packet Delivery Ratio (PDR) and could be considered as much less vulnerable against DDoS attacks prevailing in wireless healthcare sector using MANET.

This paper proposes a remote healthcare system which is referred to as Telemedicine. Telemedicine is a platform that estableshes a connection between the patient and the doctor. This platform belongs to the internet of medical things (IoMT) by enabling multiple medical sensors to connect to a server either using multiple communication technologies such as Wi-Fi, Bluetooth or GSM technologies. The system collects data from several sensors and sends them using one of the aforementioned technologies using an Arduino Board, while the interface is made with matlab and C#. Additionally, a comparative study between the three communications technologies used to connect the medical sensors to the server is investigated. And a recommendation of the best suited technology is provided with regard to the nature of the application itself.

There is a critical need for the remote monitoring of patients, especially given the current pandemic situation. This is ever more acute in developing nations, where access to healthcare is reliant on already stretched medical services. Despite this, the traditional mode of operation remains Face-to-Face (F2F), even for routine follow-up examinations. The work herein details an Internet-of-Things (IoT) based prototype that allows remote monitoring of patients in a cost-effective way. The prototype was designed to be employed in environments where resources are limited. It is particularly useful in the monitoring of patients that require constant surveillance but not necessarily immediate attention, such as the elderly or those recovering from medical procedures. A combination of sensors (including an LM35 for body temperatures) is connected to an Arduino UNO main controller board. Sensed data is processed both on-site and sent to a cloud-based system comprising of both a database and a web-application. Alerts are sent at the local and cloud levels should readings indicate potential health issues, with medical professionals able to access patient data easily. In this way, patients can be safely monitored remotely without the need for physically present caregivers, and thus also decrease the burden on a nation’s overly strained medical personnel.

There is a vision for smartphone-based medical tricorders, in which a software application can be downloaded to smartphones around the world in people's pockets to use for medical sensing at the point of care. The power of this vision is that a quick software update could allow any phone to count coughs, track anemia, detect sleep apnea, and much more. It has the potential to enable remote healthcare effectively and equalize the reach of care. However, there is a need for further discussion around the implicit difficulties to developing and deploying medical sensing applications. The reality is that every smartphone is different, and that an application developed for one phone has no guarantee of working for the next phone or even the next OS update. In this paper, our goal is to draw out motifs around the challenges that are inherent to realizing this vision of smartphone-based medical sensing solutions. We further expound on this by juxtaposing the challenges with potential futures that we organize as tenets for how we could achieve more sustainable and universal development towards smartphone-based medical tricorders.

In the next few years, fundamental technological transitions are expected both for wireless communications, soon resulting in the 5G era, and for the kind of pervasiveness that will be achieved thanks to the Internet of Things. The implementation of such new communication paradigms is expected to significantly revolutionize people’s lives, industry, commerce, and many daily activities. Healthcare applications are considered to be one of the most impacted industries. Sadly, in relation to the COVID-19 pandemic currently afflicting our society, health remote monitoring has become a fundamental and urgent application. The overcrowding of hospitals and medical facilities due to COVID-19, has unavoidably created delays and key issues in providing adequate medical assistance. In several cases, asymptomatic or light symptomatic COVID-19 patients have to be continuously monitored to prevent emergencies, and such an activity does not necessarily require hospitalization. Considering this research direction, this paper investigates the potentiality of cloud-based cellular networks to support remote healthcare monitoring applications implemented in accordance with the IoT paradigm, combined with future cellular systems. The idea is to conveniently replace the physical interaction between patients and doctors with a reliable virtual one, so that hospital services can be reserved for emergencies. Specifically, we investigate the feasibility and effectiveness of remote healthcare monitoring by evaluating its impact on the network performance. Furthermore, we discuss the potentiality of medical data compression and how it can be exploited to reduce the traffic load.

Technology has become an indispensable and intrinsic element and impacts how we all inhabit and perform tasks. IoT is a structure that permits developers to associate different devices simultaneously for health monitoring. The role of smart device healthcare applications is anticipated to become more predominant with an upsurge in the implementation of devices. In recent years, cellular networks provided uninterrupted access to the Internet. To be utilized for long-term monitoring, smart health monitoring systems are required to persuade explicit medical and functional design prerequisites. Moreover, the restriction of hardware materials is an extensive responsibility for smart health monitoring. Considered as a biomedical instrument, the smart device for remote health monitoring ought to be tremendously advanced and precise and, also, to instigate emergency services in a critical situation.

Considering the risks and difficulties of providing in-patient medical services, this is of great significance to develop mobile applications which are able to provide remote healthcare. This paper proposes a Real-time Health Monitoring Mobile Application that enables patients to remotely report health cues to healthcare teams and receive treatment plans. This allows healthcare team to collect and analyse blood oxygen, heart rate and body temperature which are reported by a Bluetooth interconnected sensory device. Yet, a chat environment is provided that allows patients to submit self-questionaries and communicate with healthcare teams in real time. In addition, this allows healthcare teams to access the patients’ health records and profiles and remotely monitor their health status. According to the test results (i.e., functional, system, front-end, back-end and hardware), this mobile application is able to offer a number of promising benefits -mainly remote basic checkup with minimum contact risks especially during COVID-19 pandemic.

With the current situation of corona pandemic, the roots of all hospital managements have been shaken, which has resulted in a need for a remote healthcare system. This can reduce the physical presence of patients and give a common and safe platform for everyone. Considering such a remote healthcare system, there are two major setbacks which need to be addressed, viz. Accountability and Security. In this proposed model, we are addressing both the issues using Blockchain as a Service. Here, the service provides additional functionality to the remote healthcare system without interfering in its operations. Further by including service, no one can delete or tamper the data resolving both the issues. An additional feature of Blockchain as a Service is that the cost of achieving these features gets reduced as this service can be integrated with multiple systems without its development and maintenance costs. Accountability refers to the ownership of data, in which once the data recorded for any user cannot be denied by him any time after that. Security here refers to data security in which data tampering any data loss is avoided. Implementing Blockchain here enhances the data security of the application.

Sensing and cloud technologies are two primary ingredients for developing IoT services. However, there are several challenges which need to be handled for delivering uninterrupted sensing services over the internet. These challenges include: handling heterogeneous sensing devices, non-interoperability issues due to different standards and protocols, acquiring sensing devices from external network by IoT applications, reducing data transmission from the sensing devices to cloud, and handling huge amounts of unstructured and semi-structured data from the cloud. Over the last decade, many IoT platforms have been proposed by the researchers, and solutions have been developed based on these platforms. However, the platforms are non-interoperable, and solutions are developed based on their own IoT infrastructure, devices, APIs, and data formats. In this research work, a framework is proposed based on sensor virtualization at the IaaS layer of the cloud. Sensors are treated as the members of a shared resource pool, just like computational resources in a cloud platform. These sensors can be acquired on a demand basis, shared by different applications, and released when no longer required by the applications. An architecture is developed for plugging the physical sensors with the cloud platform and virtualizing them to handle heterogeneity and facilitate shared access. The proposed solution has been implemented in remote healthcare delivery applications. In this chapter, a kiosk-based application is discussed, and its implementation and its impact on the society are presented as a case study.

Remote healthcare monitoring systems have increasingly become a reliable solution for delivering personalized, less intrusive and patient-friendly healthcare services. Internet of Things technology is an important driver for sustaining a large range of the capabilities of these systems, including the access to a huge amount of various health data. As this data are considered highly sensitive due to the personal feature, security and privacy threats and attacks are very likely to target the vulnerabilities of the IoT devices, network connectivity, data storage etc. This presents a brief overview of the vulnerabilities of the above-mentioned systems and a proposed solution for addressing them from the designing stage. For this purpose, a synthesis of security and privacy vulnerabilities, basic requirements and countermeasures associated distinctively with each level of the IoT-based architecture of RO-SmartAgeing system is put forward.

The ubiquitous use of digital technology in all domains and availability of Internet on smart, hand-held devices has made users’ privacy vulnerable. The technological use in remote healthcare monitoring is no exception. Remote monitoring of patients’ physiological parameters involves a body area network consisting of various sensor nodes and a coordinator node that gathers all sensed values and sends them to a health server over Internet. Since the patients’ data is exposed to the Internet, it is imperative to take measures for its protection and avoid misuse. The access to the data should be provided only after proper authentication. In this paper, we propose, Aadhaar-based authentication mechanism for patients’ medical data, wherein access to patients’ electronic medical record at various medical centers like clinics, hospitals, pathology, imaging centers, etc. is controlled by Aadhaar-based biometric or one-time password authentication. We also propose a secure mechanism for electronic medical record creation using the same authentication mechanism.

The most common malignancies in the world are skin cancers, with melanomas being the most lethal. The emergence of Convolutional Neural Networks (CNNs) has provided a highly compelling method for medical diagnosis. This research therefore conducts transfer learning with grid search based hyper-parameter fine-tuning using six state-of-the-art CNN models for the classification of benign nevus and malignant melanomas, with the models then being exported, implemented, and tested on a proof-of-concept Android application. Evaluated using Dermofit Image Library and PH2 skin lesion data sets, the empirical results indicate that the ResNeXt50 model achieves the highest accuracy rate with fast execution time, and a relatively small model size. It compares favourably with other related methods for melanoma diagnosis reported in the literature.

In this chapter, an advanced telemedicine system has been presented for monitoring remotely located patients suffers from various types of diseases. Telemedicine is a multidisciplinary field, which needs expert physician and staff, which also include high cost and types of equipment to provide quality service to patients. As telemedicine systems are now capable of monitoring remote patients with greater efficiency using numbers of advance computational methods, and researchers showing great interest in this field. Telemedicine systems may support to deal with various types of biomedical signals like EEG (electroencephalography), ECG (electrocardiography), EMG (electromyography), etc. along with the integration of modern techniques. This chapter also deals with standards uses by telemedicine system along with its special features. A cloud-based workflow model for monitoring of remote patients has been proposed here. For the continuous monitoring and analysis of biomedical signals obtained from remote patients, telemedicine system may play a great role for technical, social, and cultural development of society. Here, a discussion has been made on various aspects of the monitoring system and different issues with latest development and improvement in this field.

We describe a digital platform developed in collaboration with clinicians and user groups to provide remote healthcare monitoring and support in a dementia care application. The platform uses data from sensory devices that are deployed in participants’ homes and utilises a set machine learning and analytical algorithms to identify risks of adverse health conditions such as Urinary Tract Infections (UTIs) and hypertension in people with dementia. The platform includes a clinical interface that is used by a monitoring team to view alerts and notifications that are generated by the algorithms and to also browse the in-home activity and physiological data in a secure and privacy-aware system. The platform complies to the information governance requirements of the UK National Healthcare Service (NHS) and is registered as a class 1 medical device. The platform has been deployed and tested in over 150 homes.

Blockchain is the latest technology which is used in cryptocurrencies such as bitcoin and ether. Blockchain is the decentralized distributed ledger, which is based on the peer-to-peer network method. As the blockchain is mainly developed for implementation in the virtual cryptocurrency such as bitcoin, so its main purpose is clear that is security. Even though the healthcare industry is leading in majority of fields whether it is technology, equipment, researches, medicines, etc. We have even reached to remote locations through IoT devices and but one major thing is still lacking that is security of the data. Huge amount of data is being generated everyday from patient’s medical checkups, treatments, symptoms, etc., which is to be dealt with care as they are very crucial and can be tempered by hackers which can lead to serious problems. Therefore, such critical data must need to be secured with blockchain as it makes it very difficult to tamper with data. This paper deals with the implementation of the blockchain to solve the above-mentioned problems.

Speech signal processing and its recognition system have gained a lot of attention from last few years due to its widespread application. In this paper, a novel approach is proposed for diagnosis and monitoring the Parkinson’s Disease (PD) which is the second most severe neurological disease in the world. PD is a neurodegenerative disease which impairs person’s balance, motor skills, speech, and other characteristics such as decision making process, emotions, and sensation. Here, we proposed a cloud based framework for detecting and monitoring Parkinson patients that will enable healthcare service in low resource setting. In the developing countries, where most of the people do not get proper healthcare services and are not well aware of Parkinson’s disease, let alone detecting and getting healthcare for PD, this system can be very practical and useful. For this system, the patients of rural areas, patients from the regions where doctors are not available, can communicate to the doctors only if they have internet connections in their smart phones to access the cloud. Doctors can check and detect patient’s PD by checking their voice disorders or Dysphonia over cloud. With this system, a PD patient can be easily detected and diagnosed by giving their voice samples through their phones, regardless of their location. Based on the evaluation, our proposed systems are avail to achieve 96.6% accuracy in the cloud environment for detecting PD. It is expected that the proposed framework will have great potential to enable healthcare service for PD patients, who live in remote areas, especially in developing countries.

Biomedical image analysis is an integral part of the modern healthcare industry and has a huge impact on the modern world. Automated computer-aided systems are highly beneficial for fast, accurate and efficient diagnosis of the biomedical images. Remote healthcare systems allow doctors and patients to perform their jobs from separate geographic locations. Moreover, expert opinion about a patient can be obtained from a doctor who is in a different country or in some distant location within stipulated amount of time. Remote healthcare systems require digital biomedical images to be transferred over the network. But several security threats are associated with the transmission of the biomedical images. Privacy of the patients must be preserved by keeping the images safe from any unauthorized access. Moreover, the contents of the biomedical images must be preserved efficiently so that no one can tamper it. Data tampering can produce drastic results in many cases. In this work, a method for biomedical image security has been proposed. DNA encryption method is one of the emerging methods in the field of cryptography. A secure and lossless encryption method is developed in this work. Various numerical parameters are used to evaluate the performance of the proposed method which proves the effectiveness of the algorithm.

Remote healthcare monitoring for Cardiovascular Diseases (CVD) in the present lifestyle is of the utmost importance throughout the world because of high mortality rate, around 30% of deaths all over the world are due to the CVD as per the World Health Organization (WHO) statistics. With the advancement of medical industry and huge growth in IoT technology is gradually making the remote CVD monitoring a reality. During the real-time Electrocardiography (ECG) acquisition, proper detection of individual ECG beats, and the extraction of essential features from each ECG beat is crucial to automate the diagnosis process of CVD remotely. Therefore, it is necessary to explore various techniques for the detection of CVD and the complexity involved in it. This chapter does the review and covers various methods to process the ECG signal and focuses on the low complexity algorithms to extract the significant clinical features of ECG.

In this paper, Internet of Things (IoT) based healthcare monitoring system is studied. Several number of sensors are used to extract data of various health parameters of a patient, which is then reserved into the cloud. The data stored in cloud can be further displayed through website. The performance of a healthcare monitoring system is investigated where a short message service (SMS) notification helps a patient to remember the timing of medication and create an optimum surrounding as per the patient’s condition. Under any emergency condition, the system also generates list of nearby doctors using their geographical coordinates, which is given by GPS module. The closest doctor is then located by comparing the distance of each doctor from the patient using a distance algorithm.

Current telemedicine and remote healthcare applications foresee different interactions between the doctor and the patient relying on the use of commercial and medical wearable sensors and internet-based video conferencing platforms. Nevertheless, the existing applications necessarily require a contact between the patient and sensors for an objective evaluation of the patient’s state. The proposed study explored an innovative video-based solution for monitoring neurophysiological parameters of potential patients and assessing their mental state. In particular, we investigated the possibility to estimate the heart rate (HR) and eye blinks rate (EBR) of participants while performing laboratory tasks by mean of facial—video analysis. The objectives of the study were focused on: (i) assessing the effectiveness of the proposed technique in estimating the HR and EBR by comparing them with laboratory sensor-based measures and (ii) assessing the capability of the video—based technique in discriminating between the participant’s resting state (Nominal condition) and their active state (Non-nominal condition). The results demonstrated that the HR and EBR estimated through the facial—video technique or the laboratory equipment did not statistically differ (p &gt, 0.1), and that these neurophysiological parameters allowed to discriminate between the Nominal and Non-nominal states (p &lt, 0.02).

This paper presents an advanced Internet of Things point-of-care bio-fluid analyzer; a LoRa/Bluetooth-enabled electronic reader for biomedical strip-based diagnostics system for personalized monitoring. We undertake test simulations (technology trial without patient subjects) to demonstrate potential of long-range analysis, using a disposable test ‘key’ and companion Android app to form a diagnostic platform suitable for remote point-of-care screening for urinary tract infection (UTI). The 868 MHz LoRaWAN-enabled personalized monitor demonstrated sound potential with UTI test results being correctly diagnosed and transmitted to a remote secure cloud server in every case. Tests ranged over distances of 1.1–6.0 Km with radio path losses from 119–141 dB. All tests conducted were correctly and robustly received at the base station and relayed to the secure server for inspection. The UTI test strips were visually inspected for correct diagnosis based on color change and verified as 100% accurate. Results from testing across a number of regions indicate that such an Internet of Things medical solution is a robust and simple way to deliver next generation community-based smart diagnostics and disease management to best benefit patients and clinical staff alike. This significant step can be applied to any type of home or region, particularly those lacking suitable mobile signals, broadband connections, or even landlines. It brings subscription-free long-range bio-telemetry to healthcare providers and offers savings on regular clinician home visits or frequent clinic visits by the chronically ill. This paper highlights practical hurdles in establishing an Internet of Medical Things network, assisting informed deployment of similar future systems.

Convergence of technologies from several domains of computing and healthcare have aided in the creation of devices that can help health professionals in monitoring their patients remotely. An increase in networked healthcare devices has resulted in incidents related to data theft, medical identity theft and insurance fraud. In this paper, we discuss the design and implementation of a secure lightweight wearable health sensing system. The proposed system is based on an emerging security technology called Integrated Circuit Metric (ICMetric) that extracts the inherent features of a device to generate a unique device identification. In this paper, we provide details of how the physical characteristics of a health sensor can be used for the generation of hardware “fingerprints”. The obtained fingerprints are used to deliver security services like authentication, confidentiality, secure admission and symmetric key generation. The generated symmetric key is used to securely communicate the health records and data of the patient. Based on experimental results and the security analysis of the proposed scheme, it is apparent that the proposed system enables high levels of security for health monitoring in resource optimized manner.

The increase in demand of remote health monitoring for elderly motivates the development of a novel framework for decision making under realistic conditions. Existing works on health monitoring focuses more on sensor development, se- curity, data collection and feature extraction. But, there is no solution for analyzing multidimensional physiological parameters and activity recognition integrated in a single system. Existing systems compromises either on accuracy or detection time. To overcome these issues, this paper proposes a new idea for an automated Sensor based Decision Making (SbDM) framework that categorizes activity, vital parameters and uses Open Geo-spatial Consortium (OGC) standard for web enablement of sensor data. Two schemes such as abnormality detection in vital parameters and activity recognition are proposed in SbDM. SbDM combine generative Hidden Markov Model for activity prediction and discriminative Adaptive Neuro Fuzzy Inference System for accurate activity classification. These two modules are combined for early detection and accurate activity recognition. Combined activity recognition systems help to improve the reliability of the system and reduce false decision. SbDM also implements Decision Tree Pruning for abnormality detection in vital parameters. Activity and vital parameters measurements are closely related for future decision making or alert generation.

Remote Healthcare Monitoring (RHM) IoT infrastructure uses sensors and smartphones to collect vital parameters from patients. These parameters pertaining to medical records are shared with healthcare professionals at geographically distant locations to provide timely medical care. RHM applications deployed on IoT infrastructure must address the issues of security and privacy in a constrained environment. We present our H-Plane framework for RHM and propose the use of the NIST Next Generation Access Control (NGAC) framework for specifying and enforcing access control policies.

The main objective of this paper is to obtain an answer to the research question: Is it feasible to use a WiFi fingerprint-based indoor localization method for in-home monitoring? This question is highly relevant in fields such as Aging in Place or remote healthcare where continuous monitoring is essential. To answer this question, exhaustive experiments using expert systems and machine learning techniques have been performed in seven different real scenarios. The results showed success rate of 96% in estimating the location of a person inside his/her home in the best case, and an average of 89% in the seven studied scenarios. WiFi fingerprint-based location for in-home monitoring provides a precise location inside user's home, and it is robust enough to work even without an own WiFi access point, which in turn means a very affordable solution for in-home monitoring problems.

Providing quality healthcare in remote and rural areas pose several challenges like high patient-doctor ratio, and difficulty in accessing these locations. In this paper we propose to use JSON — based decision trees to assist doctors in providing medical help to such patients through dynamic service provisioning. A patient enters his preliminary symptoms details through a GUI in a mobile device, which is then sent for matching in a database which contains more detailed questionnaire for different ailments in JSON format. Based on the matches the subsequent decision tree is deployed on the user device for providing a finer — tuned diagnosis.

Remote healthcare systems help doctors diagnose, monitor and treat chronic diseases by collecting data from Implantable Medical Devices (IMDs) through base stations that are often located in the patients’ house. In the future, these systems may also support bidirectional communication, allowing remote reprogramming of IMDs. As sensitive medical data and commands to modify the IMD’s settings will be sent wirelessly, strong security and privacy mechanisms must be deployed.

With the ageing population, mobility is an important issue and it deters the elderlies to visit health clinics on a regular basis. Individuals with disabilities also face the same obstacles for their out-of-home medical visits. In addition, people living in remote areas often do not get the needed health care attention unless they are willing to spend the time, effort and cost to travel. Advances in information and telecommunication technologies have made telemedicine possible. Using the latest sensor technologies, a person's vital data can be collected in a smart home environment. The bio-information can then be transferred wirelessly or via the Internet to medical databases and the healthcare professionals. Using the appropriate sensing apparatus at a smart home setting, patients, elderlies and people with disabilities can have their health signals and information examined on a real-time and archival basis. Recovery process can be charted on a regular basis. Remote emergency alerts can be intercepted and responded quickly. Health deterioration can be monitored closely enabling corrective actions. Medical practitioners can therefore provide the necessary health-related services to more people. This paper surveys and compiles the state-of-the-art smart home technologies and telemedicine systems.

OSGi (Open Service Gateway Initiative) is now emerging as a promising software environment for remote healthcare systems. OSGi allows dynamic program loading and incremental update resulting in a very flexible system which is essential for remote healthcare system where constant program updating is needed. This flexibility however requires a very sensitive security solution instead since remote malware can also be injected through the same mechanism. This paper proposes a technique to check the security of external bundle, the program loaded dynamically. The paper shows the details of the technique and proves its validity.

Wireless communications are increasingly important in health-care applications, particularly in those that use implantable medical devices (IMDs). Such systems have many advantages in providing remote healthcare in terms of monitoring, treatment and prediction for critical cases. However, the existence of malicious adversaries, referred to as nodes, which attempt to control implanted devices, constitutes a critical risk for patients. Such adversaries may perform dangerous attacks by sending malicious commands to the IMD, and any weakness in the device authentication mechanism may result in serious problems including death. In this paper we present a physical layer (PHY) authentication technique for IMDs that does not use existing methods of cryptology. In addition to ensuring authentication, the proposed technique also provides advantages in terms of decreasing processing complexity of IMDs and enhances overall communications performance.

Background Vectorcardiogram (VCG) has been repeatedly found useful for clinical investigations. It may not substitute but complement Standard 12-Lead (S12) ECG. There was tremendous research between 1950s to mid-1980s on VCG in general and Frank’s System in particular, however, in last three decades it has been dropped as a routine cardiac test, the major reasons being unconventional electrode placements which required training of the physicians, greater number of electrodes involved when used to supplement S12 system and additional hardware complexity involved, at least in the early days. Although it lost the interest of cardiologists, the engineering community has adopted the VCG as a tool for interdisciplinary research. We envisage that, if accurate Frank’s VCG system is made available avoiding the aforementioned limitations, VCG will complement S12 system in diagnosis of cardiovascular diseases (CVDs). Methods and results In this paper, we propose a methodology to construct Frank VCG from S12 system using Principal Component Analysis (PCA). We have compared our work with state-of-the-art Inverse Dower Transform (IDT) and Kors Transform (KT). Mean R 2 statistics and correlation coefficient values, obtained upon comparison of reconstructed and originally measured Frank's leads, for CSE multilead (CSEDB) and PhysioNet's PTBDB databases using our proposed method, IDT and KT were found to be (73.7%,0.869), (57.6%,0.788) and (56.2%,0.781) respectively. From remote healthcare perspective, a reduced 2–3 lead system is desired and Frank lead system seems to be promising as shown by previous works. However, cardiologists are accustomed to S12 system due to its widespread usage and derived Frank lead system might not be sufficient. Hence, to bridge the gap, we have presented the results of personalized reconstruction of S12 system from derived VCG, obtained using proposed PCA-based method and compared it with results obtained when originally measured Frank leads were used. Conclusions The proposed methodology, without any modification in the current acquisition system, can be used to obtain Frank VCG from S12 system to complement it in CVD diagnosis. Omnipresent computerized machines can readily apply the proposed methodology and thus, can find widespread clinical application.

In this paper, we present the design of a mobile application to empower an integrated assistance to support users of the triad: --people with Parkinson--s disease, their carers and healthcare professionals--. Starting from a deep study of this triad--s needs, a mobile application was designed to support not just the communication between the triad users, but also to help them finding relevant knowledge to support their clinical issues as well as allowing the supervision of patients` daily routine and the recommendation for daily exercises. This application aims to facilitate the access to knowledge and provide professional support for both people with Parkinson--s disease and carers in their homes to improve the remote healthcare assistance.

Remote health care monitoring system (RHCMS) has drawn considerable attentions for the last decade. As the aging population are increasing and at the same time the health care cost is skyrocketing there has been a need to monitor a patient from a remote location. Moreover, many people of the World are out of the reach of existing healthcare systems. To solve these problems many research and commercial versions of RHCMS have been proposed and implemented till now. In these systems the performance was the main issue in order to accurately measure, record, and analyze patients’ data. With the ascent of wireless network RHCMS can be widely deployed to monitor the health condition of a patient inside and outside of the hospitals. In this work we present a ZigBee based wireless healthcare monitoring system that can provide real time online information about the health condition of a patient. The proposed system is able to send alarming messages to the healthcare professional about the patient’s critical condition. In addition the proposed system can send reports to a patient monitoring system, which can be used by the healthcare professionals to make necessary medical advices from anywhere of the World at any time.

This article belongs to the Special Issue Vehicular Communications and Networking; International audience; Recent vehicular networking activities include novel automotive applications, such as public vehicle to vehicle/infrastructure (V2X), large scale deployments, machine-to-machine (M2M) integration scenarios, and more. The platform described in this paper focuses on the integration of eHealth in a V2I setting. This is to allow the use of Internet from a vehicular setting to disseminate health-related information. From an eHealth viewpoint, the use of remote healthcare solutions to record and transmit a patient’s vital signs is a special telemedicine application that helps hospital resident health professionals to optimally prepare the patient’s admittance. From the automotive perspective, this is a typical vehicle-to-infrastructure (V2I) communication scenario. This proposal provides an IPv6 vehicular platform, which integrates eHealth devices and allows sending captured health-related data to a personal health record (PHR) application server in the IPv6 Internet. The collected data is viewed remotely by a doctor and supports his diagnostic decision. In particular, our work introduces the integration of vehicular and eHealth testbeds, describes related work and presents a lightweight auto-configuration method based on a DHCPv6 extension to provide IPv6 connectivity with a few numbers of messages

The paper presents fuzzy based approach to select best suited network for remote healthcare services. A direct mapping between clinical parameters and corresponding QoS network parameters is done. The paper proposes an application independent integrated system where stage of the disease is identified based on the fuzzified clinical parametric values which are critical for that disease. Based on critical nature of the disease i.e. stage of disease, the requirements of network QoS are defined in linguistic terms. This eliminates strict sense condition on any specific network and selects most suited network out of all available networks. The objective is to avoid denial of service in case of unavailability of a network with high QoS values and also to conserve resources in case the patient is in normal condition medically. A simulation study has been presented to verify the selection of the network based on stage of disease.

Cardiac Disease has become very common perhaps because of increasingly busy lifestyles. The rapid advancement of mobile communication technologies offers innumerable opportunities for the development of software and hardware applications for remote monitoring of chronic disease. This paper describes a remote health-monitoring service that provides an end-to-end solution. We present an efficient data mining-based solution that recognizes different CVDs (such as ventricular flutter/fibrillation, atrial fibrillation, atrial premature beat, premature ventricular contraction) from the compressed ECG, it was proposed to perform real-time classification of Cardiac Vascular Disease (CVD) based on data mining techniques. The subset of the features selection from the compressed ECG was performed using the Genetic algorithm and the clustering was performed using Expectation Maximization.

One of the major functions of telemedicine is the prompt delivery of modern healthcare to the remotest areas with reduced cost and efficient use of communication resources. The establishment of a well organized telemedicine system is therefore exigent for the developing countries like Bangladesh where there are extreme paucities of efficient healthcare professionals and equipments, specifically in the rural areas. In this paper a novel, hierarchical and semi-centralized telemedicine network architecture has been proposed holistically focusing on the rural underdeveloped areas of Bangladesh. The model utilizes the existing fiber optic backbone and wireless telecommunication infrastructures to connect the remote healthcare centers with the urban specialized hospitals. The proposed network is of low cost, flexible and faster as well as more concrete than the existing healthcare organogram of Bangladesh. Finally, some features and services associated with the model have also been proposed which are pragmatic and easily implementable.

In this paper, we describe the architecture of Sqwelch, and show how it can be applied in the delivery of healthcare to patients in the community and to support their caregiver networks. We show, through a detailed scenario, how personalized web applications can be composed by non technical users on our website: www.sqwelch.com (on which we have a demonstration video). Sqwelch uses lightweight semantics to effect mediation between heterogeneous components.

For the lack of effective methods of service aggregation in convergent network and diversification of user needs, we proposed a user preferences-driven dynamic service aggregation model (UPDDSAM) and the corresponding aggregation algorithm. This model implements run-time aggregation of service processes and service resources by adopting dynamic rules and late-binding technology. At the same time we also presents an ontology model of user preferences based on service scenario and user status, making the service aggregation more user-centric. Finally, through the case study of a practical application “Remote Healthcare”, we verify the validity of the model.

In the context of healthcare and the assistance of dependent people, this paper describes an original proactive solution for home monitoring. Our approach does not use any additional sensors and is only based on the observation of existing home automation events. An analysis of user habits is proposed to offer automatic scenarios which adapt to user capabilities. The simulation results, issued from previous experiments, show that our approach helps improve user autonomy while facilitating their use of daily services.

Major benefits may be achieved in the efficient administration of healthcare provision if a secure and adaptable communications system could be established to allow patient and medical data to be communicated to remote locations. However, significant barriers exist preventing the uptake of this technology. This paper addresses a number of these concerns via the integration of technology allowing for the efficient generation of encryption keys directly from the characteristics of electronic devices (ICmetrics) and the utilisation of flexible communication pathways within enclosed environments offered by the incorporation of intelligent physical panels constructed from frequency selective surfaces (FSS) within building designs. (4 pages)

� Abstract—Clinicians and patients need mobile tools to detect ototoxic change early and prevent hearing loss. We report on the development of an upgrade of our existing desktop-based clinical-audiological instrumentation into a mobile instrument platform which efficiently supports personalized ototoxicity monitoring on the hospital wards as well as clinic by a trained clinician. Our new wireless-enabled system also serves as the instrumentation platform for the next phase of our work which is remote healthcare delivery with patient-guided at-home ototoxicity monitoring using an evidence-based individualized SRO protocol. I. INTRODUCTION

Smart homes are intelligent environments that interact with their occupants in ways that improve their lives. This paper presents the ubiquitous pervasive technologies that will need to be integrated to realise the future vision of smart homes that have the intelligence to anticipate, predict and take decisions in a virtually autonomous manner. It also presents some of the major application areas that will benefit from the realisation of smart homes. It then discusses some of the challenges that need to be overcome in order for smart homes to become a reality and find acceptance from people across all sectors of the society.

Many studies have shown that mobile phones are in great demand; smart phones in particular have an ever-growing market. It is evident that with greater number of mobile users, people can be facilitated with mobile accessible applications. Of many beneficial applications one such application is providing remote healthcare on mobile devices. Much work has been done in this area of research; however using Bluetooth enabled smart phones with Wireless Application Protocol (WAP) technology implementing an intelligent remote healthcare system has been proposed. A rule based expert system can identify diagnosis for remotely located patients to off location expert physicians and subsequent analysis can be delivered to the patient relieving her from frequent visits to healthcare service provider thus reducing the load of patients on existing infrastructure. This paper presents design and implementation of Mobile Intelligent Remote Healthcare Monitoring System (MIRHMS) and discusses issues.

This research is in the field of remote healthcare monitoring systems which propose software solutions to monitor elderly people in their own homes. Our objective is to take advantage of the technological diversity of several Decision Support Systems used to detect distress situations. We propose a multi-agent approach in which each agent encapsulates a decision support system. This encapsulation enables the real-time combination of decisions. In this paper, we present the architecture of our multi-agent system and the real-time scheduling of the collective decision process.

Physiological or biological stress is an organism’s response to a stressor such as an environmental condition or a stimulus. The identification of physiological stress while performing the activities of daily living is an important field of health research in preventive medicine. Activities initiate a dynamic physiological response that can be used as an indicator of the overall health status. This is especially relevant to high risk groups; the assessment of the physical state of patients with cardiovascular diseases in daily activities is still very difficult. This paper presents a context-aware telemonitoring platform, IPM-mHealth, that receives vital parameters from multiple sensors for online, real-time analysis. IPM-mHealth provides the technical basis for effectively evaluating patients’ physiological conditions, whether inpatient or at home, through the relevance between physical function and daily activities. The two core modules in the platform include: 1) online activity recognition algorithms based on 3-axis acceleration sensors and 2) a knowledge-based, conditional-reasoning decision module which uses context information to improve the accuracy of determining the occurrence of a potentially dangerous abnormal heart rate. Finally, we present relevant experiments to collect cardiac information and upper-body acceleration data from the human subjects. The test results show that this platform has enormous potential for use in long-term health observation, and can help us define an optimal patient activity profile through the automatic activity analysis.