Intelligent Health Management Research Articles

Emerging Multimodel Zirconia Nanosystems for High‐Performance Biomedical Applications

Advancement in nanotechnology supports system development to achieve rapid, affordable, and intelligent health management and has raised the urgent demand to explore multimodel affordable metal oxide nanostructures with the features of biocompatible tunable performance and scaled‐up processing. Keeping this as a motivation, zirconia nanostructures (Zr‐NSs) are emerging as nanostructures of choice for advanced biomedical applications due to affordable, scalable production, easy surface modifications, tunable surface morphology, multiphase stability, and acceptability by biological features such as biocompatibility, viability for bioactives, and a high isoelectric point of 9.5. Zr‐NSs, alone and in the form of hybrids, and nanocomposites have demonstrated notable high performance to develop next‐generation biosensors, implanted materials, and bioelectronics. However, their excellent salient features toward high‐performance biomedical system development are not well articulated in the form of a review. To overcome this knowledge gap, herein an attempt is made to summarize state‐of‐the‐art Zr‐NS preparation techniques as per targeted applications, surface functionalization of Zr‐NSs to achieve desired properties, and applications of Zr‐NSs in the field of biomedicine for health wellness. The challenges, possible solutions, and authors’ viewpoints considering prospects in mind are also part of this report.

Optimization of Data Processing System for Exercise and Fitness Process Based on Internet of Things

In the digital network era, people have higher requirements for physical fitness. In the future, physical fitness requires not only good fitness equipment and fitness environment but also more convenient and intelligent health management, service guidance, social entertainment, and other refined fitness services. The innovation of sports and fitness equipment for the digital network era will definitely depend on the development of information technology and network technology. Based on the cutting‐edge Internet of Things technology, this thesis focuses on the development and application of a new generation of digital fitness equipment adapted to future development, advocating the new concept of seamless integration of fitness exercise and information services through human‐oriented systematic design thinking and providing implementable solutions to realize the science, convenience, and life of public fitness. This thesis uses modern science and technology, especially the Internet of Things (IoT) technology, to fully meet the diversified fitness needs of the fitness crowd as the guide; IoT digital fitness equipment design and application research was newly generated, using a variety of research methods to explore the functional design and application of IoT fitness equipment; the goal is to create a more intelligent and three‐dimensional IoT fitness service model in the future. Through the application research of intelligent devices in IoT fitness equipment, the realization of the functions of identity identification, environment perception, and data transmission of IoT fitness equipment is made faster. Intelligent devices can become the interaction channel between fitness service personnel, fitness equipment, and fitness users and also reduce the development cost of IoT fitness equipment. The construction of an IoT fitness cloud service platform and data management system integrates the application of IoT, cloud computing, mobile communication, and other technologies to make IoT fitness service supply remote, real‐time, and diversified. While providing convenient and value‐added fitness services for fitness people, it also brings sustainable development space for the health service industry.

Digital twin approach for intelligent operation planning and health management of mechanical systems

The digital twin paradigm aims to fuse information obtained from sensor data, physics models, and operational data for a mechanical component in use to make well-informed decisions regarding health management and operations of the component. In this work, we discuss a methodology for digital-twin-based operation planning in mechanical systems to enable: a) cost-effective maintenance scheduling, and b) resilient operations of the system. As properties of mechanical systems, as well as their operational parameters, loads and environment are stochastic in nature, our methodology includes probabilistic damage diagnosis, probabilistic damage prognosis, and system optimization under uncertainty. As an illustrative example, we consider the problem of fatigue crack growth in a metal component. We discuss a probabilistic, ultrasonic-guided-wave-based crack diagnosis framework that can handle both aleatory and epistemic uncertainties in the diagnosis process. We build a high-fidelity, finite element model to simulate the piezoelectric effect and ultrasonic guided wave propagation. We use test data obtained by conducting diagnostic experiments on the physical twin to calibrate the error in the diagnosis model. We perform Bayesian diagnosis of crack growth using the corrected diagnosis model, considering data corrupted by measurement noise, and fuse the information from multiple sensors. We build a finite-element-based high-fidelity model for crack growth under uniaxial cyclic loading, and calibrate a phenomenological (low-fidelity) fatigue crack growth model using the high-fidelity model output as well as data from fatigue loading experiments performed on the physical twin. We use the resulting multi-fidelity model in a probabilistic crack growth prognosis framework; thus achieving both accuracy and computational efficiency. Lastly, we utilize the damage diagnosis framework along with the damage prognosis model to optimize system operations under diagnostic and prognostic uncertainty. We perform simulation as well as laboratory experiments that show how the digital twin of the component of interest can be used for intelligent health management and operation planning for mechanical systems.

A Phenomenological Study on New Care Needs of Maslow's Need-Hierarchy Among Disabled Residents at Nursing Homes in Modern Chinese Society.

Meeting the care needs of disabled residents of nursing homes is a vital element to providing high-quality care in modern society. Therefore, this study aimed to explore the disabled residents' new care needs in nursing homes based on Maslow's hierarchy of needs theory from their perspectives in modern society. This was a qualitative study. Semi-structured interviews were conducted with a purposeful sample of 23 disabled residents in a nursing home in China. All data were transcribed and coded for emerging themes. A qualitative data analysis generated 4 dimensions and 12 aspects of needs: intelligent technology (including intelligent health management, intelligent life care, and intelligent psychological care), security (including air and food safety, network security, and protecting privacy), participation (including social relations, self-regulated learning, and volunteer service), and spirituality (including religious beliefs needs, maintaining dignity, and self-determination). These findings can inform nursing home health care professionals about the new care needs of disabled residents to improve nursing home care protocols to accommodate residents' expressed needs, thus fostering better care.

Artificial intelligence-aided decision support in paediatrics clinical diagnosis: development and future prospects.

Artificial intelligence (AI)-aided decision support has developed rapidly to meet the needs for effective analysis of substantial data sets from electronic medical records and medical images generated daily, and computer-assisted intelligent drug design. In clinical practice, paediatricians make medical decisions after obtaining a large amount of information about symptoms, physical examinations, laboratory test indicators, special examinations and treatments. This information is used in combination with paediatricians’ knowledge and experience to form the basis of clinical decisions. This diagnosis and therapeutic strategy development based on large amounts of information storage can be applied to both large clinical databases and data for individual patients. To date, AI applications have been of great value in intelligent diagnosis and treatment, intelligent image recognition, research and development of intelligent drugs and intelligent health management. This review aims to summarize recent advances in the research and clinical use of AI in paediatrics.

Automotive IVHM: Towards Intelligent Personalised Systems Healthcare

AbstractUnderpinned by a contemporary view of automotive systems as cyber-physical systems, characterised by progressively open architectures increasingly defined by their interaction with the users and the smart environment, this paper provides a critical and up-to-date review of automotive Integrated Vehicle Health Management (IVHM) systems. The paper discusses the challenges with prognostics and intelligent health management of automotive systems, and proposes a high-level framework, referred to as the Automotive Healthcare Analytic Factory, to systematically collect and process heterogeneous data from across the product lifecycle, towards actionable insight for personalised healthcare of systems.

Medical data fusion algorithm based on Internet of things

In order to explore the data fusion algorithm in medical Internet of things, the monitoring of medical data in the Internet of things is discussed and studied focusing on data fusion and related routing technology. According to the particularity of the data in the medical Internet of things, a data fusion cluster-tree construction algorithm based on event-driven (DFCTA) is proposed. The fusion delay problem in the network is analyzed, and the minimum fusion delay method is proposed by calculation of the fusion waiting time of the nodes. Finally, the intelligent health management data fusion system in the medical Internet of things is designed. Aiming at the characteristics of multilevel integration of multisource heterogeneous data fusion for intelligent health management, the data fusion architecture of fusion tree composed of fusion nodes is proposed. The experiment shows that the DFCTA algorithm has good fusion performance. Based on the above findings, it is concluded that the algorithm is a fast and reliable method, which has important practical significance.

An Architecture for Intelligent Systems Based on Smart Sensors

Based on requirements for a next-generation rocket test facility, elements of a prototype intelligent rocket test facility (IRTF) have been implemented. The preliminary results provide the basis for future advanced development and validation using rocket test stand facilities at Stennis Space Center (SSC). Key components include distributed smart sensor elements integrated using a knowledge-driven environment. One of the specific goals is to imbue sensors with the intelligence needed to perform self-diagnosis of health and to participate in a hierarchy of health determination at sensor, process, and system levels. We have identified issues important to further development of health-enabled networks, which should be of interest to others working with smart sensors and intelligent health management systems.

One summer at NASA's John C. Stennis Space Center

The test stands at SSC are enormous structures, each with several hundred - even thousands - of sensors. With such a high sensor count and variety it is incredibly difficult to do anything useful with the influx of data in real time beyond recording it for future examination. However, it would be ideal if the test controller could not only view all of the data in an intuitive and information rich environment but also have the complementary sensor data fused together to provide more robust information. Enter the integrated intelligent health management system (IIHMS). The IIHMS is a collection of smart sensors, each aware of the local process they are measuring, controlled by an expert system with a global view of the entire system. The long-range goal is to develop an expert system to provide the test controller with a fully immersed environment in which to monitor the condition of every system component and receive advice on anomalies and proper courses of action. The IIHMS is now termed intelligent system health management (ISHM). Dave was assigned the hardware design of a smart temperature sensor that would form the foundation layer of the IIHMS; Don was responsible for the firmware that would add the intelligence to Dave's hardware by enabling it to monitor the health (i.e., quality) of the sensor and the quality of measurements in real time; Jon was left the task of designing the link between the smart sensor and the expert system. This work discusses the summer experience of the authors at SSC.