Real-time Healthcare Services Research Articles

An integrating computing framework based on edge-fog-cloud for internet of healthcare things applications

History has demonstrated that healthcare and medical systems play a crucial role in enforcing the development of science and technology. Humans have been seeing an explosive growth of e-health applications, where cloud computing has dominated e-healthcare systems and all domains in the past decades. However, one primary barrier to cloud-based e-health systems is their high response time. In emergencies, data aggregation and handling and decision-making regarding treatment need to be performed in seconds and directly affect the life of patients. These issues relate to resource constraints, such as medical equipment, ambulances, medical staff, medical facilities, and other conditions. To address these problems, computing architectures have been introduced, such as edge, fog, and cloud computing methods. In this work, we propose an all-in-one computing framework based on combining different computing solutions in real-time smart healthcare domains. Then, considering the efficiency of the proposed solution by the queue network model tool, we give recommendations for the optimal network infrastructure deployment depending on the scale-patients of hospitals. We believe that the results of this work will play an important role in the network infrastructure optimization of medical facilities while ensuring the provision of real-time healthcare services.

Restricted Boltzmann Machine Assisted Secure Serverless Edge System for Internet of Medical Things.

The Internet of things (IoT) is a network of technologies that support a wide variety of healthcare workflow applications to facilitate users' obtaining real-time healthcare services. Many patients and doctors' hospitals use different healthcare services to monitor their healthcare and save their records on the servers. Healthcare sensors are widely linked to the outside world for different disease classifications and questions. These applications are extraordinarily dynamic and use mobile devices to roam several locales. However, healthcare apps confront two significant challenges: data privacy and the cost of application execution services. This work presents the mobility-aware security dynamic service composition (MSDSC) algorithmic framework for workflow healthcare based on serverless, serverless, and restricted Boltzmann machine mechanisms. The study suggests the stochastic deep neural network trains probabilistic models at each phase of the process, including service composition, task sequencing, security, and scheduling. The experimental setup and findings revealed that the developed system-based methods outperform traditional methods by 25% in terms of safety and 35% in application cost.

Balancing Technological Advances with User Needs: User-centered Principles for AI-Driven Smart City Healthcare Monitoring

In recent years, the integration of artificial intelligence (AI) technologies has greatly benefited smart city healthcare, meeting the growing demand for affordable, efficient, and real-time healthcare services. Patient monitoring is one area where artificial intelligence has shown great promise. Improved health outcomes have been made possible by the advancement of AI-based monitoring systems, which enable more personalized and continuous patient monitoring. However, to fully maximize the benefits of these systems, a user-centered approach is essential, which prioritizes patients' needs and experiences while ensuring their privacy and autonomy are respected. This study focuses on the application of user-centered design principles in the development and deployment of AI-driven monitoring systems in smart city healthcare. Addressing the challenges and opportunities of AI-driven monitoring systems, the article considers issues such as privacy and security concerns, data accuracy, and user acceptance. Finally, some possible future directions to the challenges are suggested. A user-centered approach to AI monitoring systems is recommended for healthcare providers to enhance patient experience in smart city healthcare.

Internet of medical things-based real-time digital health service for precision medicine: Empirical studies using MEDBIZ platform.

The aim of this study was to introduce the implemented MEDBIZ platform based on the internet of medical things (IoMT) supporting real-time digital health services for precision medicine. In addition, we demonstrated four empirical studies of the digital health ecosystem that could provide real-time healthcare services based on IoMT using real-world data from in-hospital and out-hospital patients. Implemented MEDBIZ platform based on the IoMT devices and big data to provide digital healthcare services to the enterprise and users. The big data platform is consisting of four main components: IoMT, core, analytics, and services. Among the implemented MEDBIZ platform, we performed four clinical trials that designed monitoring services related to chronic obstructive pulmonary disease, metabolic syndrome, arrhythmia, and diabetes mellitus. Of the four empirical studies on monitoring services, two had been completed and the rest were still in progress. In the metabolic syndrome monitoring service, two studies were reported. One was reported that intervention components, especially wearable devices and mobile apps, made systolic blood pressure, diastolic blood pressure, waist circumference, and glycosylated hemoglobin decrease after 6 months. Another one was presented that increasing high-density lipoprotein cholesterol and triglyceride levels were prevented in participants with the pre-metabolic syndrome. Also, self-care using healthcare devices might help prevent and manage metabolic syndrome. In the arrhythmia monitoring service, during the real-time monitoring of vital signs remotely at the monitoring center, 318 (15.9%) general hikers found abnormal signals, and 296 (93.1%) people were recommended for treatment. We demonstrated the implemented MEDBIZ platform based on IoMT supporting digital healthcare services by acquiring real-world data for getting real-world evidence. And then through this platform, we were developing software as a medical device, digital therapeutics, and digital healthcare services, and contributing to the development of the digital health ecosystem.

Privacy Aware Internet of Medical Things Data Certification Framework on Healthcare Blockchain of 5G Edge

The 5G communication technology and edge computing jointly form 5G Edge and eliminate the constraints of Internet-of-Medical Things (IoMT) devices to facilitate several real-time healthcare services, including patient monitoring and diagnoses from anywhere. Nevertheless, the 5G Edge platform introduces risks of internal attacks to the data integrity and privacy of IoMT data. Hence, end users cannot trust data retrieved from the 5G Edge. This paper proposes a privacy-preserving search result certification framework for IoMT data on 5G and edge computing-assisted blockchain networks. The novelty of this paper is twofold. First, the proposed framework introduces a blockchain platform involving 5G Edge servers to ensure tamperproof data storage. In addition, an encrypted data storage model and a symmetric-key cryptography-based privacy-preserving search mechanism are developed for the 5G Edge blockchain. Second, the framework leverages an efficient multi-signature scheme to design a tamperproof search result certification mechanism for IoMT data on the blockchain of 5G Edge. Several experiments are conducted to evaluate the performance of the proposed framework.

Distributed Pricing Policy for Cloud-Assisted Body-to-Body Networks with Optimal QoS and Energy Considerations

Cloud-Assisted Wireless Body Area Networks (WBANs) is one of the key emerging technology for medical applications in the era of Internet-of-Things. In cloud-assisted WBANs, the WBANs are able to access the cloud resources, which are geographically close to the local Access Points (APs). However, in the presence of poor radio link-quality between cloud-assisted WBANs and APs, energy consumption and service delay of the network dynamically changed. Therefore, in order to solve this problem, we propose an energy-efficient Body-to-Body (B2B) communications among coexisting WBANs to relay the information of the patients to nearest APs to provide real-time healthcare services. On the other hand, the healthcare services enabled for cloud-assisted WBANs is popularly known as Healthcare-as-a-Service (He-aaS). In He-aaS, it is very challenging to finalize a price agreement between WBANs and Health-Cloud Service Providers (H-CSPs) as WBANs follow a heterogeneous architecture. The existing pricing mechanisms, which are constrained to the homogeneous applications, are not compatible to He-aaS. We propose an optimal and distributed pricing policy for He-aaS in order to increase the profit level of each WBAN with the consideration of the optimal QoS and energy. Analysis of the proposed algorithms and the inferences of the results validates the usefulness of the proposed energy-efficient B2B communication and distributed pricing policy. Extensive results indicate that our proposed schemes are able to efficiently utilize limited resource with optimal QoS and energy consideration.

Medical Image Forgery Detection for Smart Healthcare

With the invention of new communication technologies, new features and facilities are provided in a smart healthcare framework. The features and facilities aim to provide a seamless, easy-to-use, accurate, and real-time healthcare service to clients. As health is a sensitive issue, it should be taken care of with utmost security and caution. This article proposes a new medical image forgery detection system for the healthcare framework to verify that images related to healthcare are not changed or altered. The system works on a noise map of an image, applies a multi-resolution regression filter on the noise map, and feeds the output to support-vector-machine-based and extreme-learning-based classifiers. The noise map is created in an edge computing resource, while the filtering and classification are done in a core cloud computing resource. In this way, the system works seamlessly and in real time. The bandwidth requirement of the proposed system is also reasonable.

SDN-Based Multi-Tier Computing and Communication Architecture for Pervasive Healthcare

A large spectrum of healthcare applications, ranging from continuous blood sugar level monitoring to sleep apnea detection are nowadays facilitated by modern mobile gadgets. Wearable and ambient sensors generate enormous amounts of physiological data that demand high computation power for real-time processing and large storage area for recording the personal data. In order to conserve the energy on the battery-limited mainstream mobile devices of the end-users, the execution of the healthcare applications may be offloaded to a remote server. While cloud computing provides unlimited pool of resources for latency-tolerant services such as training a machine learning model, the personalization of healthcare services and the delay sensitivity of continuous health assessment necessitate a computation infrastructure in the vicinity of the end-users. As a remedy to address various demands of a wide range of pervasive healthcare applications, we propose a multi-tier computing and communication architecture composed of end-user devices, edge servers, and legacy cloud data-centers. The dynamic management of this architecture, policies to be applied within the network and the orchestration of the healthcare services are carried out by the concept of programmable networks, in the form of software-defined networking (SDN). As a concrete demonstration of our ideas, a fall risk assessment service is implemented and an experimental study is conducted to evaluate its accuracy and the performance of the multi-tier architecture. The results indicate that the proposed architecture is feasible to enable real-time healthcare services and has significant performance advantages over traditional cloud-based approaches.

Smart healthcare monitoring: a voice pathology detection paradigm for smart cities

With the increasing demand for automated, remote, intelligent, and real-time healthcare services in smart cities, smart healthcare monitoring is necessary to provide improved and complete care to residents. In this monitoring, health-related media or signals collected from smart-devices/objects are transmitted and processed to cater to the need for quality care. However, it is challenging to create a framework or method to handle media-related healthcare data analytics or signals (e.g., voice/audio, video, or electroglottographic (EGG) signals) to meet the complex on-demand healthcare needs for successful smart city management. To this end, this paper proposes a cloud-oriented smart healthcare monitoring framework that interacts with surrounding smart devices, environments, and smart city stakeholders for affordable and accessible healthcare. As a smart city healthcare monitoring case study, a voice pathology detection (VPD) method is proposed. In the proposed method, two types of input, a voice signal and an EGG signal, are used. The input devices are connected to the Internet and the captured signals are transmitted to the cloud. The signals are then processed and classified as either normal or pathologic with a confidence score. These results are passed to registered doctors that make the final decision and take appropriate action. To process the signals, local features are extracted from the first-order derivative of the voice signal, and shape and cepstral features are extracted from the EGG signal. For classification, a Gaussian mixture model-based approach is used. Experimental results show that the proposed method can achieve VPD that is more than 93% accurate.

Secure IoT framework and 2D architecture for End-To-End security

In this paper, we proposed an secure IoT framework to ensure an End-To-End security from an IoT application to IoT devices. The proposed IoT framework consists of the IoT application, an IoT broker and the IoT devices. The IoT devices can be deployed along a board line or a boundary of the area of IoT broker. The IoT broker manages their own devices and aggregates their sensing data. The IoT application provides users with IoT services. To use the IoT services, it needs to access to sensing data. Especially, the case of real-time healthcare services should consider intermediate security issues because medical information of patients is one of very sensitive privacy information. However, most of IoT protocols such as CoAP and MQTT have no concern about the End-To-End security, they only depended on the security of DTLS. Therefore, we proposed a new IoT framework to satisfy the End-To-End security feature under the CoAP communication. The proposed framework encrypts sensitive data by a symmetric encryption and an attribute-based encryption for efficiencies of communication and computation costs. In addition, each IoT device has a unique identification used as one of their attributes. Consequently, although the IoT broker is one of the intermediate nodes, it decrypts and shows data only if it satisfies all attributes.

Intelligent healthcare service based on context inference using smart device

In the field of "U-Healthcare Service", many studies have been actively conducted to develop "smart device"-based healthcare applications that enable healthcare providers and patients to be better served through interoperations among various kinds of sensors and wireless network interfaces. In particular, contemporary intelligent healthcare services not only recognize users' context information through smart devices, computers, and so forth, but also acquire information from heterogeneous sensors to achieve context-aware inference services. Among such information, the weather information is tightly related to diseases such as asthma and allergies. Therefore, there is a high demand for research to utilize the weather information for healthcare services. In this paper, we propose a context inference-based intelligent healthcare service that exploits both the weather conditions information and the diverse healthcare ontologies available on the Internet. The proposed service aims at modeling a context ontology in users' healthcare service environment and defining the inference rules, thereby accomplishing a satisfactory real-time healthcare service.

A Study of the Recent Technological Advancements for the Near and Far Future of u-Healthcare Systems

This paper presents a discussion of the significant trends to technological developments that hold relevance to the evolution of u-Healthcare systems and analyzes the opportunities they may offer to enhance real-time healthcare services. The developments in digital revolution are identified such as location based services, Mobile IPv6, wireless body sensor networks, the evolution of wireless technologies, and smart devices. The convergence of this these technological developments have given a direct impact to the near and far future of uhealthcare systems.