Biblio

Remote patient monitoring is a system that focuses on patients care and attention with the advent of the Internet of Things (IoT). The technology makes it easier to track distance, but also to diagnose and provide critical attention and service on demand so that billions of people are safer and more safe. Skincare monitoring is one of the growing fields of medical care which requires IoT monitoring, because there is an increasing number of patients, but cures are restricted to the number of available dermatologists. The IoT-based skin monitoring system produces and store volumes of private medical data at the cloud from which the skin experts can access it at remote locations. Such large-scale data are highly vulnerable and otherwise have catastrophic results for privacy and security mechanisms. Medical organizations currently do not concentrate much on maintaining safety and privacy, which are of major importance in the field. This paper provides an IoT based skin surveillance system based on a blockchain data protection and safety mechanism. A secure data transmission mechanism for IoT devices used in a distributed architecture is proposed. Privacy is assured through a unique key to identify each user when he registers. The principle of blockchain also addresses security issues through the generation of hash functions on every transaction variable. We use blockchain consortiums that meet our criteria in a decentralized environment for controlled access. The solutions proposed allow IoT based skin surveillance systems to privately and securely store and share medical data over the network without disturbance.

Internet of Things (IoT) is experiencing significant growth in the safety-critical applications which have caused new security challenges. These devices are becoming targets for different types of physical attacks, which are exacerbated by their diversity and accessibility. Therefore, there is a strict necessity to support embedded software developers to identify and remediate the vulnerabilities and create resilient applications against such attacks. In this paper, we propose a hardware security vulnerability assessment based on fault injection of an embedded application. In our security assessment, we apply a fault injection attack by using our clock glitch generator on a critical medical IoT device. Furthermore, we analyze the potential risks of ignoring these attacks in this embedded application. The results will inform the embedded software developers of various security risks and the required steps to improve the security of similar MCU-based applications. Our hardware security assessment approach is easy to apply and can lead to secure embedded IoT applications against fault attacks.

A recent study featuring a new kind of care robot indicated that participants expect a robot's ethical decision-making to be transparent to develop trust, even though the same type of `inspection of thoughts' isn't expected of a human carer. At first glance, this might suggest that robot transparency mechanisms are required for users to develop trust in robot-made ethical decisions. But the participants were found to desire transparency only when they didn't know the specifics of a human-robot social interaction. Humans trust others without observing their thoughts, which implies other means of determining trustworthiness. The study reported here suggests that the method is social interaction and observation, signifying that trust is a social construct. Moreover, that `social determinants of trust' are the transparent elements. This socially determined behaviour draws on notions of virtue ethics. If a caregiver (nurse or robot) consistently provides good, ethical care, then patients can trust that caregiver to do so often. The same social determinants may apply to care robots and thus it ought to be possible to trust them without the ability to see their thoughts. This study suggests why transparency mechanisms may not be effective in helping to develop trust in care robot ethical decision-making. It suggests that roboticists need to build sociable elements into care robots to help patients to develop patient trust in the care robot's ethical decision-making.

Health Information Technology Systems (HITS) are increasingly used to improve the quality of patient care while reducing costs. These systems have been developed in response to the changing models of care to an ongoing relationship between patient and care team, supported by the use of technology due to the increased instance of chronic disease. However, the use of HITS may increase the risk to patient safety and security. While standards can be used to address and manage these risks, significant communication problems exist between experts working in different departments. These departments operate in silos often leading to communication breakdowns. For example, risk management stakeholders who are not clinicians may struggle to understand, define and manage risks associated with these systems when talking to medical professionals as they do not understand medical terminology or the associated care processes. In order to overcome this communication problem, we propose the use of the “Three Amigos” approach together with the use of the SIMPLE tool that has been developed to assist patients in understanding medical terms. This paper examines how the “Three Amigos” approach and the SIMPLE tool can be used to improve estimation of severity of risk by non-clinical risk management stakeholders and provides a practical example of their use in a ten step risk management process.

Advances in new Communication and Information innovations has led to a new paradigm known as Internet of Things (IoT). Healthcare environment uses IoT technologies for Patients care which can be used in various medical applications. Patient information is encrypted consistently to maintain the access of therapeutic records by authoritative entities. Healthcare Internet of Things (HIoT) facilitate the access of Patient files immediately in emergency situations. In the proposed system, the Patient directly provides the Key to the Doctor in normal care access. In Emergency care, a Patient shares an Attribute based Key with a set of Emergency Supporting Representatives (ESRs) and access permission to the Doctor for utilizing Emergency key from ESR. The Doctor decrypts the medical records by using Attribute based key and Emergency key to save the Patient's life. The proposed model Secure Information Retrieval using Lightweight Cryptography (SIRLC) reduces the secret key generation time and cipher text size. The performance evaluation indicates that SIRLC is a better option to utilize in Healthcare IoT than Lightweight Break-glass Access Control(LiBAC) with enhanced security and reduced computational complexity.

Emergency medical services universally get regarded as the essential part of the health care delivery system [1]. A relationship exists between the emergency patient death rate and factors such as the failure to access a patient's critical data and the time it takes to arrive at hospitals. Nearly thirty million Americans do not live within an hour of trauma care, so this poor access to trauma centers links to higher pre-hospital death rates in more than half of the United States [2]. So, we need to address the problem. In a patient care-cycle, loads of medical data items are born in different healthcare settings using a disparate system of records during patient visits. The ability for medical care providers to access a patient's complete picture of emergency-relevant medical data is critical and can significantly reduce the annual mortality rate. Today, the problem exists with a continuous recording system of the patient data between healthcare providers. In this paper, we've introduced a combination of secure file transfer methods/tools and blockchain technology as a solution to record patient Emergency relevant medical data as patient walk through from one clinic/medical facility to another, creating a continuous footprint of patient as a secure and scalable data source. So, ambulance crews can access and use it to provide high quality pre-hospital care. All concerns of medical record sharing and accessing like authentication, privacy, security, scalability and audibility, confidentiality has been considered in this approach.

Recently, the home healthcare system has emerged as one of the most useful technology for e-healthcare. Contrary to classical recording methods of patient's medical data, which are, based on paper documents, nowadays all this sensitive data can be managed and forwarded through digital systems. These make possible for both patients and healthcare workers to access medical data or receive remote medical treatment using wireless interfaces whenever and wherever. However, simplifying access to these sensitive and private data can directly put patient's health and life in danger. In this paper, we propose a secure and lightweight biometric-based remote patient authentication scheme using elliptic curve encryption through which two mobile healthcare system communication parties could authenticate each other in public mobile healthcare environments. The security and performance analysis demonstrate that our proposal achieves better security than other concurrent schemes, with lower storage, communication and computation costs.

In this paper, we report our work on using machine learning techniques to predict back bending activity based on field data acquired in a local nursing home. The data are recorded by a privacy-aware compliance tracking system (PACTS). The objective of PACTS is to detect back-bending activities and issue real-time alerts to the participant when she bends her back excessively, which we hope could help the participant form good habits of using proper body mechanics when performing lifting/pulling tasks. We show that our algorithms can differentiate nursing staffs baseline and high-level bending activities by using human skeleton data without any expert rules.

Technological advances in wearable and implanted medical devices are enabling wireless body area networks to alter the current landscape of medical and healthcare applications. These systems have the potential to significantly improve real time patient monitoring, provide accurate diagnosis and deliver faster treatment. In spite of their growth, securing the sensitive medical and patient data relayed in these networks to protect patients' privacy and safety still remains an open challenge. The resource constraints of wireless medical sensors limit the adoption of traditional security measures in this domain. In this work, we propose a distributed mobile agent based intrusion detection system to secure these networks. Specifically, our autonomous mobile agents use machine learning algorithms to perform local and network level anomaly detection to detect various security attacks targeted on healthcare systems. Simulation results show that our system performs efficiently with high detection accuracy and low energy consumption.

An intelligent recovery evaluation system is presented for objective assessment and performance monitoring of anterior cruciate ligament reconstructed (ACL-R) subjects. The system acquires 3-D kinematics of tibiofemoral joint and electromyography (EMG) data from surrounding muscles during various ambulatory and balance testing activities through wireless body-mounted inertial and EMG sensors, respectively. An integrated feature set is generated based on different features extracted from data collected for each activity. The fuzzy clustering and adaptive neuro-fuzzy inference techniques are applied to these integrated feature sets in order to provide different recovery progress assessment indicators (e.g., current stage of recovery, percentage of recovery progress as compared to healthy group, etc.) for ACL-R subjects. The system was trained and tested on data collected from a group of healthy and ACL-R subjects. For recovery stage identification, the average testing accuracy of the system was found above 95% (95-99%) for ambulatory activities and above 80% (80-84%) for balance testing activities. The overall recovery evaluation performed by the proposed system was found consistent with the assessment made by the physiotherapists using standard subjective/objective scores. The validated system can potentially be used as a decision supporting tool by physiatrists, physiotherapists, and clinicians for quantitative rehabilitation analysis of ACL-R subjects in conjunction with the existing recovery monitoring systems.
 

Care of chronic cardiac patients requires information interchange between patients' homes, clinical environments, and the electronic health record. Standards are emerging to support clinical information collection, exchange and management and to overcome information fragmentation and actors delocalization. Heterogeneity of information sources at patients' homes calls for open solutions to collect and accommodate multidomain information, including environmental data. Based on the experience gained in a European Research Program, this paper presents an integrated and open approach for clinical data interchange in cardiac telemonitoring applications. This interchange is supported by the use of standards following the indications provided by the national authorities of the countries involved. Taking into account the requirements provided by the medical staff involved in the project, the authors designed and implemented a prototypal middleware, based on a service-oriented architecture approach, to give a structured and robust tool to congestive heart failure patients for their personalized telemonitoring. The middleware is represented by a health record management service, whose interface is compliant to the healthcare services specification project Retrieve, Locate and Update Service standard (Level 0), which allows communication between the agents involved through the exchange of Clinical Document Architecture Release 2 documents. Three performance tests were carried out and showed that the prototype completely fulfilled all requirements indicated by the medical staff; however, certain aspects, such as authentication, security and scalability, should be deeply analyzed within a future engineering phase.