A comprehensive survey on multimodal medical signals fusion for smart healthcare systems

Smart healthcare is a framework that utilizes technologies such as wearable devices, the Internet of Medical Things (IoMT), sophisticated machine learning algorithms, and wireless communication technology to seamlessly access health records, link individuals, resources, and organizations, and then effectively handle and react to health environment demands intelligently. One of the main ingredients of smart healthcare is medical sensors or IoMT. Due to the complex nature of diseases, in many cases, there is a need for multimodal medical signals for their diagnoses. While using multimodal signals, the most important issue is how to fuse them – an area of burgeoning interest within the research community. This paper presents a comprehensive survey of multimodal medical signals fusion schemes that have been proposed for smart healthcare applications. Research works included in major repositories, such as IEEE Xplore, Science Direct, Springer Link, and ACM digital library have been surveyed to address several related research questions. Focus is placed on recent developments, thus only works published between 2014-2020 are considered. Finally, key research challenges and possible future directions are also provided.     

Internet of Things in Healthcare: Architecture,Applications, Challenges,and Solutions

Healthcare, the largest global industry, is undergoing significant transformations with the genesis of a new technology known as the Internet of Things (IoT). Many healthcare leaders are investing more money for transforming their services to harness the benefits provided by IoT, thereby paving the way for the Internet of Medical Things (IoMT), an extensive collection of medical sensors and associated infrastructure. IoMT has many benefits like providing remote healthcare by monitoring health vitals of patients at a distant place, providing healthcare services to elderly people, and monitoring a large group of people in a region or country for detection and prevention of epidemics. This paper provides a review of IoT in the healthcare domain by first describing the enabling technologies for delivering smart healthcare, followed by some of the key applications of IoT in healthcare. Next, a fog-based architecture consisting of three layers for IoT-based healthcare applications is proposed. Finally, we focus on some of the open challenges of IoT in healthcare, like fault tolerance, interoperability, latency, energy efficiency, and availability. Existing solutions for these challenges are also discussed.     

Virtual Nursing Using Deep Belief Networks for Elderly People (DBN-EP)

The demand for better health services has resulted in the advancement of remote monitoring health, i.e., virtual nursing systems, to watch and support the elderly with innovative concepts such as being patient-centric, easier to use, and having smarter interactions and more accurate conclusions. While virtual nursing services attempt to provide consumers and medical practitioners with continuous medical and health monitoring services, access to allied healthcare experts such as nurses remains a challenge. In this research, we present Virtual Nursing Using Deep Belief Networks for Elderly People (DBN-EP), a new framework that provides a virtual nurse agent deployed on a senior citizen’s home, workplace, or care centre to help manage their health condition on a continuous basis. Using this method, healthcare providers can assign various jobs to nurses by utilizing a general task definition mechanism, in which a task is defined as a combination of medical workflow, operational guidelines, and data gathered from a remotely monitored virtual nursing system. Practitioners are in charge of DBN-EP and make treatment decisions for patients. This allows a DBN-EP to act as a personalized full-time nurse for a client by carrying out practitioner support activities based on information gathered about the client’s health. An electronic Personal Health Record (ePHR) system, such as a specialized web portal and mobile apps, could provide such patient information to elderly person family members and care centres. We created a prototype system using a DBN-EP system that allows traditional client applications and healthcare provider systems to collaborate. Finally, we demonstrate how this system may benefit the elderly through a result and debate.     

Proof of Activity Protocol for IoMT Data Security

The Internet of Medical Things (IoMT) is an online device that senses and transmits medical data from users to physicians within a time interval. In, recent years, IoMT has rapidly grown in the medical field to provide healthcare services without physical appearance. With the use of sensors, IoMT applications are used in healthcare management. In such applications, one of the most important factors is data security, given that its transmission over the network may cause obtrusion. For data security in IoMT systems, blockchain is used due to its numerous blocks for secure data storage. In this study, Blockchain-assisted secure data management framework (BSDMF) and Proof of Activity (PoA) protocol using malicious code detection algorithm is used in the proposed data security for the healthcare system. The main aim is to enhance the data security over the networks. The PoA protocol enhances high security of data from the literature review. By replacing the malicious node from the block, the PoA can provide high security for medical data in the blockchain. Comparison with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio, precision ratio, security, and efficiency and less response time for Blockchain-enabled healthcare systems.     

Hybrid Smart Contracts for Securing IoMT Data

Data management becomes essential component of patient healthcare. Internet of Medical Things (IoMT) performs a wireless communication between E-medical applications and human being. Instead of consulting a doctor in the hospital, patients get health related information remotely from the physician. The main issues in the E-Medical application are lack of safety, security and privacy preservation of patient’s health care data. To overcome these issues, this work proposes block chain based IoMT Processed with Hybrid consensus protocol for secured storage. Patients health data is collected from physician, smart devices etc. The main goal is to store this highly valuable health related data in a secure, safety, easy access and less cost-effective manner. In this research we combine two smart contracts such as Practical Byzantine Fault Tolerance with proof of work (PBFT-PoW). The implementation is done using cloud technology setup with smart contracts (PBFT-PoW). The accuracy rate of PBFT is 90.15%, for PoW is 92.75% and our proposed work PBFT-PoW is 99.88%.     

A Novel Internet of Medical Thing Cryptosystem Based on Jigsaw Transformation and Ikeda Chaotic Map

Image encryption has attracted much interest as a robust security solution for preventing unauthorized access to critical image data. Medical picture encryption is a crucial step in many cloud-based and healthcare applications. In this study, a strong cryptosystem based on a 2D chaotic map and Jigsaw transformation is presented for the encryption of medical photos in private Internet of Medical Things (IoMT) and cloud storage. A disorganized three-dimensional map is the foundation of the proposed cipher. The dispersion of pixel values and the permutation of their places in this map are accomplished using a nonlinear encoding process. The suggested cryptosystem enhances the security of the delivered medical images by performing many operations. To validate the efficiency of the recommended cryptosystem, various medical image kinds are used, each with its unique characteristics. Several measures are used to evaluate the proposed cryptosystem, which all support its robust security. The simulation results confirm the supplied cryptosystem’s secrecy. Furthermore, it provides strong robustness and suggested protection standards for cloud service applications, healthcare, and IoMT. It is seen that the proposed 3D chaotic cryptosystem obtains an average entropy of 7.9998, which is near its most excellent value of 8, and a typical NPCR value of 99.62%, which is also near its extreme value of 99.60%. Moreover, the recommended cryptosystem outperforms conventional security systems across the test assessment criteria.     

Context-aware hybrid reasoning framework for pervasive healthcare

Pervasive computing has emerged as a viable solution capable of providing technology-driven assistive living for elderly. The pervasive healthcare system, Context-Aware Real-time Assistant (CARA), is designed to provide personalized healthcare services for elderly in a timely and appropriate manner by adapting the healthcare technology to fit in with normal activities of the elderly and working practices of the caregivers. The work in this paper introduces a personalized, flexible, and extensible hybrid reasoning framework for CARA system in a smart home environment which provides context-aware sensor data fusion as well as anomaly detection mechanisms that supports activity of daily living analysis and alert generation. We study how the incorporation of rule-based and case-based reasoning enables CARA to become more robust and to adapt to a changing environment by continuously retraining with new cases. Noteworthy about the work is the use of case-based reasoning to detect conditional anomalies for home automation, and the use of hierarchical fuzzy rule-based reasoning to deal with exceptions and to achieve query-sensitive case retrieval and case adaptation. Case study for evaluation of this hybrid reasoning framework is carried out under simulated but realistic smart home scenarios. The results indicate the feasibility of the framework for effective at-home monitoring.     

Sleeping situation monitoring system in ubiquitous environments

In recent years, because of the development of ubiquitous technology in health care, research is actively progress. We describe a sleeping situation monitoring system, created to support home healthcare services. We discuss the method we used to develop the system and how to use the sleep activity monitor to support home health care. Information about the sleeping situation is collected from motion detection, sound, and vibration sensors. And this information is based on real-time processing, we used the TMO (Time-trigger and Message-trigger Object) schema and TMOSM (TMO Support Middleware) into the development software environment of the healthcare application. To verify the practical use of sleeping situation information as recorded by the system discussed in this paper, we relate an example of the monitoring of a sleeping situation using our system, and we describe the results of an experimental evaluation.     

面向智慧医疗的家庭健康跟踪系统

智慧医疗是物联网的重点应用领域,家庭健康跟踪系统是实现智慧医疗的重要组成部分.本文在开源硬件平台Raspberry Pi和Arduino的基础之上,设计和实现了一款家庭健康跟踪系统.该系统利用Arduino实现健康生理信息的采集、集成和蓝牙传输,在Raspberry PI平台上利用QT实现健康状况的分析,并最终在LCD屏幕上实现健康状况的可视化.最后,通过原型系统实现和测试,证明了该系统的可行性和有用性.本文设计的家庭健康跟踪系统具备操作简单、可扩展性强和成本低等特点,可为智慧医疗的全面实现提供基础.     

Paillier Cryptography Based Message Authentication Code for IoMT Security

Health care visualization through Internet of Things (IoT) over wireless sensor network (WSN) becomes a current research attention due to medical sensor evolution of devices. The digital technology-based communication system is widely used in all application. Internet of medical thing (IoMT) assisted healthcare application ensures the continuous health monitoring of a patient and provides the early awareness of the one who is suffered without human participation. These smart medical devices may consume with limited resources and also the data generated by these devices are large in size. These IoMT based applications suffer from the issues such as security, anonymity, privacy, and interoperability. To overcome these issues, data aggregation methods are the solution that can concatenate the data generated by the sensors and forward it into the base station through fog node with efficient encryption and decryption. This article proposed a well-organized data aggregation and secured transmission approach. The data generated by the sensor are collected and compressed. Aggregator nodes (AN) received the compressed data and concatenate it. The concatenated and encrypted data is forward to fog node using the enhanced Paillier cryptography-based encryption with Message Authentication code (MAC). Fog node extracts the forwarded data from AN using Fog message extractor method (FME) with decryption. The proposed system ensures data integrity, security and also protects from security threats. This proposed model is simulated in Network Simulator 2.35 and the evaluated simulation results proves that the aggregation with MAC code will ensures the security, privacy and also reduces the communication cost. Fog node usages in between Aggregator and base station, will reduce the cloud server/base station computational overhead and storage cost. The proposed ideology is compared with existing data aggregation schemes in terms of computational cost, storage cost, communication cost and energy cost. Cost of communication takes 18.7 ms which is much lesser than existing schemes.     

Online clinical decision support system using optimal deep neural networks

The Internet of Health things (IoHT) has numerous applications in healthcare by integrating health monitoring things like sensors and medical devices for remotely observe patient’s records to provide smarter and intelligent medicare services. To avail best healthcare services to the users using the e-health applications, in this paper, we propose an IoT with cloud based clinical decision support system for the prediction and observance of Chronic Kidney Disease (CKD) with its level of severity. The proposed framework collects the patient data using the IoT devices attached to the user which will be stored in the cloud along with the related medical records from the UCI repository. Furthermore, we employ a Deep Neural Network (DNN) classifier for the prediction of CKD and its level of severity. A Particle Swarm Optimization (PSO) based feature selection method is also used to improve the performance of DNN classifier. The proposed model is validated by employing the benchmark CKD dataset. Different classifiers are employed to compare the performance of the proposed model under several classification measures. The proposed DNN classifier alone predicts CKD with an accuracy of 98.25% and is further enhanced to 99.25 by PSO-FS method. At the same time, the improved classification performance is verified with higher values of 98.03 specificity, 99.25 accuracy, 99.39 F-score and 98.40 kappa value respectively.     

电子保健体系及其关键技术分析

关注健康的人们正面对这样一个现实：健康信息零散地掌握在个人、医生、医院、机构甚至雇主手中,不成体系,缺乏逻辑关联.建立电子保健体系就是要集成涵盖从生到死整个历程的保健资源,它依托互联网,随时随地向个人、医生及相关机构提供安全、完整和规范的保健信息.探讨电子保健体系的框架结构、基本功能、相关国际规范以及关键技术.分析建立该体系面临的主要困难和制约因素,提出一套较为可行的实施方案.     

综合应力下综合控制器PHM试验平台设计

为了模拟综合控制器真实的工作环境,解决综合控制器故障样本与故障验证环境不足的问题,以便对综合控制器进行健康状态评估,进行了综合控制器PHM试验平台的设计.首先进行了PHM试验平台总体框架分析,确定了设计内容,然后针对综合控制器环境考核需求,明确了各单项应力的参数,制定了综合控制器综合环境应力试验方案,接着基于PCI-9114数据采集卡以及LabVIEW设计综合控制器数据采集系统,实现对综合控制器各个模块的多个状态参数进行实时监测.试验结果表明:该系统稳定可靠,操作简单,实用性强,并能够实现对综合控制器状态参数的实时采集、自动存储,以及事后查阅,从而为综合控制器健康状态评估提供数据支撑.     

Authentication of lossy data in body-sensor networks for cloud-based healthcare monitoring

Growing pressure on healthcare costs is spurring development of lightweight bodyworn sensors for real-time and continuous physiological monitoring. Data from these sensors is streamed wirelessly to a handheld device such as a smartphone or tablet, and then archived in the cloud by personal health record services. Authenticating the data these devices generate is vital to ensure proper diagnosis, traceability, and validation of claims. Digital signatures at the packet-level are too resource-intensive for bodyworn devices, while block-level signatures are not robust to loss. In this paper we propose, analyse, and validate a practical, lightweight robust authentication scheme suitable for health-monitoring. We make three specific contributions: (a) we develop an authentication scheme that is both low-cost (using a Merkle hash tree to amortise digital signature costs), and loss-resilient (using network coding to recover strategic nodes within the tree). (b) We design a framework for optimizing placement of network coding within the tree to maximise data verifiability for a given overhead and loss environment. (c) We validate our scheme using experimental traces of typical operating conditions to show that it achieves high success (over 99% of the medical data can be authenticated) at very low overheads (as low as 5% extra transmissions) and at very low cost (the bodyworn device has to perform a digital signature operation no more than once per hour). We believe our novel authentication scheme can be a key step in the integration of wearable medical monitoring devices into current cloud-based healthcare systems.     

An Immutable Framework for Smart Healthcare Using Blockchain Technology

The advancements in sensing technologies, information processing, and communication schemes have revolutionized the healthcare sector. Electronic Healthcare Records (EHR) facilitate the patients, doctors, hospitals, and other stakeholders to maintain valuable data and medical records. The traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks. A single attempt of a successful Denial of Service (DoS) attack can compromise the complete healthcare system. This article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things (IoMT) to address the stated challenges. The proposed architecture is on the idea of a lightweight private blockchain-based network that facilitates the users and hospitals to perform multiple healthcare-related operations in a secure and trustworthy manner. The efficacy of the proposed framework is evaluated in the context of service execution time and throughput. The experimental outcomes indicate that the proposed design attained lower service execution time and higher throughput under different control parameters.     

Body sensor networks for ubiquitous healthcare

Body sensor networks provide a platform for ubiquitous healthcare, driving the diagnosis in hospital static environment to the daily life dynamic context. We realized the importance of sensing of activities, which is not only a dimension of human health but also important context information for diagnosis based on the physiologic data. This paper presents our ubiquitous healthcare system, uCare. It consists of uCare devices and a server system. Currently, the uCare system is designed for cardiovascular disease (CVD) examination and management. The uCare device has been tested in a trial in Beijing Hospital. The uCare system will be further tested in elderly care at home and exercise management in training to measure heart dynamics during training.     

Extreme learning machine and bayesian optimization-driven intelligent framework for IoMT cyber-attack detection

The Journal of Supercomputing - The Internet of Medical Things (IoMT) is a bionetwork of allied medical devices, sensors, wearable biosensor devices, etc. It is gradually reforming the healthcare...     

基于国产X86处理器的异构计算平台构建及敏感数据保护

大数据时代对敏感数据的保护需求与日俱增,如何在安全可控的软硬件环境下进行大数据集处理成为一个研究热点。设计了一种基于安全可控国产X86处理器的大数据平台,利用AES(Advanced Encryption Standard)算法对海量敏感数据提供安全保障;并合理构建GPU异构计算环境,充分提高国产大数据平台的分析计算效率,为海量数据的安全处理提供了全新的解决方案。实验结果表明,基于国产兆芯X86处理器的GPU异构计算平台能有效满足大数据集处理需求;通过改进异构计算环境下的AES算法提升了加密效率,并获得了22~23倍的加速比。当应对海量数据(GB级以上)时,国产异构计算平台的并行处理能力和加速效果非常明显。该研究结果对有海量敏感信息的大数据集处理和信息安全保护具有重要应用价值。     

Convergence agent model for developing u-healthcare systems

Ubiquitous networking, the interlinking of computers, consumer electronics, automobiles, home appliances, medical equipment, transportation systems, animals and plants through an overlapping of electronic spaces and physical spaces, is expected to provide an environment that enhances our lives through improved convenience, efficiency and safety in all areas.Today’s healthcare industry emphasizes safety, efficiency, patient-oriented approach, timeliness and balance. u-healthcare makes it possible to safely deliver appropriate services from any location at any time. To explain the emergence of u-healthcare, one must invoke, first of all, progress in IT and medical technology, and then the desire and willingness on the part of health care institutions to adopt the new service concept and increased health care demand. Digitalization of information, introduction of broadband communication and leaps made in healthcare technology in recent years have provided the technological capacity necessary for the achievement of u-healthcare.Based on recently reached theoretical results, integrating u-healthcare environments in virtual organizations, we proposed a methodology for the design and implementation of u-healthcare, linking the distributed mobile agents with medical entities into a convergence and collaborative environment. Despite the challenges in implementing and deploying u-healthcare, the advantages of ubiquitous healthcare are enabled by our smart model for the soft computing endowed ubiquitous. The strength of our approach is that it relates to u-healthcare a system which consists of its dual character emerging from the synergetic interaction between ubiquitous computing techniques and the mobile devices in convergence manner. We have developed a set of initial healthcare agent services on the mobile collaboration framework. We evaluate the functionality and effectiveness of our convergence mobile agent, whether the platform can manage services based on the model and whether the results of u-healthcare services discovery could satisfy user’s requirements with example cases.     

Social value creation through digital activism in an online health community

The study explores how online health communities produce social value by uniting individuals under a common purpose, to advance healthcare in post‐conflict states. We selected MedicineAfrica – a digital platform known for creating social value by providing medical education in regions with under‐resourced healthcare systems – and drew on multiple data collection methods. We found that it is through a unique form of digital health activism that social value is created in this context. Drawing on a sociological understanding of digital health activism, we make the following contributions: First, we identify three types of non‐economic, social value: cognitive, professional and epistemic. Second, we indicate that social value creation is enabled by three emergent forms of digital health activism (ie, philanthropic, moral and reciprocal activity). Third, we elicit three enabling mechanisms explaining how these forms of activism are technically and socially afforded through the platform's connective capacity and emerging collective practices in tandem with its members' growing commitment. Our article contributes to the growing IS literature on digital activism by offering a framework that elucidates how digital health activism relates to social value creation. The article provides practical implications as to how platforms can enable sustainable online (health) communities.