Intelligent Health Management Research Articles

Rapid response, superior stable, and durable pressure sensor with rGO/CNC interdigital electrode

The development of pressure sensors with swift responsiveness and exceedingly high stability is an inevitable requirement for wearable electronic devices. Herein, we fabricated a high performance pressure sensor through the encapsulation of an interdigital electrode with employing polydimethylsiloxane (PDMS). The pressure sensor comprised self-assembled multifunctional reduced graphene oxide/cellulose nanocrystalline (rGO/CNC) composite film with interdigital electrode (RCI), and involves the use of PDMS to enhance the robustness and pressure sensitivity of the pressure sensor. The sensing enhancement is reflected in minimal conductive attenuation, under 30 min ultrasonic with a ΔR/R0 < 0.1 % in the initial phase and an impressive response time of 51 ms. Due to the outstanding performance, a smart glove that is integrated with the RCI sensor not only performs well timely feedback on human motions, but also achieves intelligent recognitions of various gestures assisted by neural network algorithms (the accuracy up to 99.1 %). Furthermore, this study offers an innovative decoupling method that effectively separates contact interface resistance and body resistance. The developed piezoresistive sensor and decoupling method are meaningful and valuable to long-term intelligent health management and human–machine interactions applications.

Revolutionizing digital healthcare networks with wearable strain sensors using sustainable fibers

AbstractWearable strain sensors have attracted research interest owing to their potential within digital healthcare, offering smarter tracking, efficient diagnostics, and lower costs. Unlike rigid sensors, fiber‐based ones compete with their flexibility, durability, adaptability to body structures as well as eco‐friendliness to environment. Here, the sustainable fiber‐based wearable strain sensors for digital health are reviewed, and material, fabrication, and practical healthcare aspects are explored. Typical strain sensors predicated on various sensing modalities, be it resistive, capacitive, piezoelectric, or triboelectric, are explained and analyzed according to their strengths and weaknesses toward fabrication and applications. The applications in digital healthcare spanning from body area sensing networks, intelligent health management, and medical rehabilitation to multifunctional healthcare systems are also evaluated. Moreover, to create a more complete digital health network, wired and wireless methods of data collection and examples of machine learning are elaborated in detail. Finally, the prevailing challenges and prospective insights into the advancement of novel fibers, enhancement of sensing precision and wearability, and the establishment of seamlessly integrated systems are critically summarized and offered. This endeavor not only encapsulates the present landscape but also lays the foundation for future breakthroughs in fiber‐based wearable strain sensor technology within the domain of digital health.

Research on Abnormal Behavior Detection Technology for Simmental Cattle

This paper mainly studies the abnormal behavior detection technology of Simmental cattle, aiming to establish an efficient and reliable abnormal behavior detection system, so as to detect abnormal situations in time and take corresponding measures to deal with potential problems. At the same time, by establishing a dataset for the abnormal behavior of Simmental cattle, the study uses deep learning algorithms to accurately capture the existence, location and key body parts of Simmental cattle, and accurately identify abnormal behaviors such as convulsions and falls. The application of abnormal behavior detection technology to animal husbandry to achieve contactless, automated, and efficient monitoring of Simmental cattle behavior can provide advanced and comprehensive intelligent health management solutions for animal husbandry and promote the wide application of intelligent management in animal husbandry. Real-time monitoring, early warning and fine management of Simmental cattle behavior can effectively reduce the risk of disease transmission and improve the production safety of farms.

Micromesh reinforced strain sensor with high stretchability and stability for full‐range and periodic human motions monitoring

AbstractThe development of strain sensors with high stretchability and stability is an inevitable requirement for achieving full‐range and long‐term use of wearable electronic devices. Herein, a resistive micromesh reinforced strain sensor (MRSS) with high stretchability and stability is prepared, consisting of a laser‐scribed graphene (LSG) layer and two styrene‐block‐poly(ethylene‐ran‐butylene)‐block‐poly‐styrene micromesh layers embedded in Ecoflex. The micromesh reinforced structure endows the MRSS with combined characteristics of a high stretchability (120%), excellent stability (with a repetition error of 0.8% after 11 000 cycles), and outstanding sensitivity (gauge factor up to 2692 beyond 100%). Impressively, the MRSS can still be used continauously within the working range without damage, even if stretched to 300%. Furthermore, compared with different structure sensors, the mechanism of the MRSS with high stretchability and stability is elucidated. What's more, a multilayer finite element model, based on the layered structure of the LSG and the morphology of the cracks, is proposed to investigate the strain sensing behavior and failure mechanism of the MRSS. Finally, due to the outstanding performance, the MRSS not only performes well in monitoring full‐range human motions, but also achieves intelligent recognitions of various respiratory activities and gestures assisted by neural network algorithms (the accuracy up to 94.29% and 100%, respectively). This work provides a new approach for designing high‐performance resistive strain sensors and shows great potential in full‐range and long‐term intelligent health management and human–machine interactions applications.image

A Survey on Impact of Internet of Medical Things Against Diabetic Foot Ulcer

INTRODUCTION: In this study, we explore the intricate domain of Diabetic Foot Ulcers (DFU) through the development of a comprehensive framework that encompasses diverse operational scenarios. The focus lies on the identification and classification assessment of diabetic foot ulcers, the implementation of smart health management strategies, and the collection, analysis, and intelligent interpretation of data related to diabetic foot ulcers. The framework introduces an innovative approach to predicting diabetic foot ulcers and their key characteristics, offering a technical solution for forecasting. The exploration delves into various computational strategies designed for intelligent health analysis tailored to patients with diabetic foot ulcers.&#x0D; OBJECTIVES: The primary objective of this paper is to present a technical solution for forecasting diabetic foot ulcers, utilizing computational strategies for intelligent health analysis.&#x0D; METHODS: Techniques derived from social network analysis are employed to conduct this research, focusing on diverse computational strategies geared towards intelligent health analysis for patients with diabetic foot ulcers. The study highlights methodologies addressing the unique challenges posed by diabetic foot ulcers, with a central emphasis on the integration of Internet of Medical Things (IoMT) in prediction strategies.&#x0D; RESULTS: The main results of this paper include the proposal of IoMT-based computing strategies covering the entire spectrum of DFU analysis, such as localization, classification assessment, intelligent health management, and detection. The study also acknowledges the challenges faced by previous research, including low classification rates and elevated false alarm rates, and proposes automatic recognition approaches leveraging advanced machine learning techniques to enhance accuracy and efficacy.&#x0D; CONCLUSION: The proposed IoMT-based computing strategies present a significant advancement in addressing the challenges associated with predicting diabetic foot ulcers. The integration of advanced machine learning techniques demonstrates promise in improving accuracy and efficiency in diabetic foot ulcer localization, marking a positive stride towards overcoming existing limitations in previous research.

Improvement of operation and maintenance efficiency of power transformers based on knowledge graphs

AbstractCurrently, the digital transformation of the power grid is underway, and the intelligent health management technology for power transformers is rapidly advancing. However, there are issues in the operation and maintenance process, such as weak information correlation and low decision‐making efficiency. Knowledge graphs have been applied in other industrial fields, such as spacecraft maintenance, to significantly improve knowledge query efficiency. However, there is a lack of literature on knowledge graph construction in the field of power transformer operation and maintenance. Additionally, there is limited publicly available data and difficulties in effectively mining operation and maintenance knowledge in this field. A method for constructing a knowledge graph for power transformer operation and maintenance based on ALBERT is proposed. Firstly, publicly available literature in the field of power transformers is collected, and a sample enhancement method using regular matching is used to enrich the semi‐structured corpora, such as power system accident investigation reports, to construct a training dataset for power transformer operation and maintenance. Then, the ALBERT‐BiLSTM‐CRF deep learning algorithm is applied to extract power transformer operation and maintenance entities from the relevant literature and accident investigation reports, and this method is compared with traditional deep learning algorithms to demonstrate its superiority. Subsequently, the ALBERT‐BiLSTM‐Attention deep learning algorithm, which incorporates ALBERT and attention mechanism, is utilised to extract relationships between power transformer operation and maintenance entities. Compared to other deep learning algorithms, this algorithm demonstrates better performance in the domain‐specific texts of power transformer operation and maintenance. Finally, the Neo4j graph database is used to visualise and present the knowledge graph, enabling decision support based on the power transformer operation and maintenance knowledge graph.

Microneedle Sensors for Point-of-Care Diagnostics.

Point-of-care (POC) has the capacity to support low-cost, accurate and real-time actionable diagnostic data. Microneedle sensors have received considerable attention as an emerging technique to evolve blood-based diagnostics owing to their direct and painless access to a rich source of biomarkers from interstitial fluid. This review systematically summarizes the recent innovations in microneedle sensors with a particular focus on their utility in POC diagnostics and personalized medicine. The integration of various sensing techniques, mostly electrochemical and optical sensing, has been established in diverse architectures of "lab-on-a-microneedle" platforms. Microneedle sensors with tailored geometries, mechanical flexibility, and biocompatibility are constructed with a variety of materials and fabrication methods. Microneedles categorized into four types: metals, inorganics, polymers, and hydrogels, have been elaborated with state-of-the-art bioengineering strategies for minimally invasive, continuous, and multiplexed sensing. Microneedle sensors have been employed to detect a wide range of biomarkers from electrolytes, metabolites, polysaccharides, nucleic acids, proteins to drugs. Insightful perspectives are outlined from biofluid, microneedles, biosensors, POC devices, and theragnostic instruments, which depict a bright future of the upcoming personalized and intelligent health management.

Design of Physical Training Assistant System Based on Intelligent Health Management System

Design of Physical Training Assistant System Based on Intelligent Health Management System

Current state of the art and future directions for implantable sensors in medical technology: Clinical needs and engineering challenges.

Implantable sensors have revolutionized the way we monitor biophysical and biochemical parameters by enabling real-time closed-loop intervention or therapy. These technologies align with the new era of healthcare known as healthcare 5.0, which encompasses smart disease control and detection, virtual care, intelligent health management, smart monitoring, and decision-making. This review explores the diverse biomedical applications of implantable temperature, mechanical, electrophysiological, optical, and electrochemical sensors. We delve into the engineering principles that serve as the foundation for their development. We also address the challenges faced by researchers and designers in bridging the gap between implantable sensor research and their clinical adoption by emphasizing the importance of careful consideration of clinical requirements and engineering challenges. We highlight the need for future research to explore issues such as long-term performance, biocompatibility, and power sources, as well as the potential for implantable sensors to transform healthcare across multiple disciplines. It is evident that implantable sensors have immense potential in the field of medical technology. However, the gap between research and clinical adoption remains wide, and there are still major obstacles to overcome before they can become a widely adopted part of medical practice.

Multiscale reduction clustering of vibration signals for unsupervised diagnosis of machine faults

Fault diagnosis is of great importance for the intelligent health management of mechanical systems. For engineering applications, it is very difficult to collect and label vibration signals corresponding to machine faults. Due to the complicated operational environment, moreover, useful and critical features are often covered by surrounding noise. For those reasons, a multiscale reduction clustering (MRC) method is proposed for the unsupervised diagnosis of machine faults. In the present approach, vibration signals were collected to generate multiscale convolutional representation through a one-dimensional convolutional neural network without prior knowledge of signal processing techniques. Chosen by a convolutional encoder, the dimensionality of the multiscale convolutional representation was reduced for improving the clustering capability. During this dimensionality reduction, a loss function was proposed to optimize the network and speed up the convergence of the diagnostics. The proposed method was evaluated by two benchmark datasets and an experimental setup. With the present method, the clustering accuracy and normalized mutual information for three datasets are all over 0.91 and 0.82, respectively. Results show that the addressed MRC has superior diagnosis ability under the unsupervised fashion compared to other state-of-the-art models. It is proved that MRC is robust for the diagnosis tasks under different working conditions.

Intelligent health management based on analysis of big data collected by wearable smart watch

Some problems still exist in health management and application such as insufficient data, limited technology, and lack of professional evaluation methods by physicians with medical theory. In this study, an intelligent method is based on an analysis of physiological big data collected by wearable smartwatches. Firstly, physiological data such as pulse, heart rate, and blood oxygen were collected continuously from individuals by wearing smartwatches, and the data was digitally transmitted. Secondly, the transmitted data was sent to a health management platform by Narrow Band Internet of Things. Analyzing the data, physicians evaluated individual situations via an intelligent math model. Finally, the results were fed back to individuals through a smartphone APP to finish a medical diagnosis, disease prediction, or warning. The intelligent health management method and technology created via years of studies have been verified and will provide a new and effective strategy for health management.

Theoretical Discussion on Intelligent Health Management Service Design

Health management is the fundamental guarantee of national health. Sports health detection aims to provide two-way communication between medical experts and patients. With the overall rise of modern information technology, how to build an intelligent health management service system is a new topic. This article discusses the feasibility and necessity of the design of intelligent health management services by using the knowledge of computer-related theoretical disciplines, hoping to provide theoretical support for health management services.

Digital twin-driven intelligent assessment of gear surface degradation

Gearbox has a compact structure, a stable transmission capability, and a high transmission efficiency. Thus, it is widely applied as a power transmission system in various applications, such as wind turbines, industrial machinery, aircraft, space vehicles, and land vehicles. The gearbox usually operates in harsh and non-stationary working environments, expediting the degradation process of the gear surface. The degradation process may lead to severe gear failures, such as tooth breakage and root crack, which could damage the gear transmission system. Therefore, it is essential to assess the progression of gear surface degradation in order to ensure a reliable operation. The digital twin is an emerging technology for machine health management. A high-fidelity digital twin model can help reflect the operation status of the gearbox and reveal the corresponding degradation mechanism, which could benefit the remaining useful life (RUL) prediction and the predictive maintenance-based decision-making framework. This paper develops a digital twin-driven intelligent health management method to monitor and assess the gear surface degradation progression. The developed method can effectively reveal the gear wear propagation characteristics and predict the RUL accurately. Furthermore, the knowledge learned from digital twin models can be well transferred to the surface wear assessment of the physical gearbox in wide industrial applications, which is of great practical significance. Two endurance tests with different dominant degradation mechanisms were conducted to validate the effectiveness of the proposed methodology for gear wear assessment.

Application of Smart Wearable Fitness Equipment and Smart Health Management Based on the Improved Algorithm.

IoT technology has scientific, advanced, and practical characteristics in wearable fitness equipment and artificial intelligence health management. On this basis, this paper discusses the intelligent health management system based on IoT technology. By building an IoT health management system, the design principles and algorithm design of wearable fitness equipment are introduced around the device, and various noises that affect the accuracy of the equipment and how to better filter out various noises are also analyzed. The authors selected cloud service platforms and also investigated and analyzed the application prospects of wearable fitness equipment in the community. From the perspective of real-time monitoring, scientific management, and intelligent decision-making, we ensure high-quality, standardized, scientific, efficient, and accurate wearable fitness equipment applications and intelligent health management. In addition to simply viewing IoT technology as an information dissemination platform, IoT technology can also expand many other functions. According to the survey results of this article, 90% of people will use the APP with the function of sports and fitness in social media. One of the most concerned issues in the current society is the application of social media in national fitness activities.

Synthesis of decision making in a distributed intelligent personnel health management system on offshore oil platform

This paper proposes a methodological approach for the decision synthesis in a geographically distributed intelligent health management system for oil workers working in offshore industry. The decision-making methodology is based on the concept of a person-centered approach to managing the health and safety of personnel, which implies the inclusion of employees as the main component in the control loop. This paper develops a functional model of the health management system for workers employed on offshore oil platforms and implements it through three phased operations that is monitoring and assessing the health indicators and environmental parameters of each employee, and making decisions. These interacting operations combine the levels of a distributed intelligent health management system. The paper offers the general principles of functioning of a distributed intelligent system for managing the health of workers in the context of structural components and computing platforms. It presents appropriate approaches to the implementation of decision support processes and describes one of the possible methods for evaluating the generated data and making decisions using fuzzy pattern recognition. The models of a fuzzy ideal image and fuzzy real images of the health status of an employee are developed and an algorithm is described for assessing the deviation of generated medical parameters from the norm. The paper also compiles the rules to form the knowledge bases of a distributed intelligent system for remote continuous monitoring. It is assumed that embedding this base into the intelligent system architecture will objectively assess the trends in the health status of workers and make informed decisions to eliminate certain problems

An Intelligent Collection System of Big Data in Medical and Health Education Based on the Internet of Things.

The Internet of Medical Things has developed rapidly as an important direction in the field of Internet of Things, especially through the use of the new generation of information technology, theoretical and applied research on medical IoT intelligent health management that provides “full-service.” It has become a research hotspot of many universities and research institutions. Therefore, conducting research on intelligent health management in the network of medical things is of great engineering importance and theoretical guidance to improve the level of medical information. For health management in the network of medical things to conduct research around the goal of health management “Everything full spectrum for everyone,” analyzed the lack of sharing of health information in current health management, lack of continuous monitoring and management of health indicators, etc., a new “individual-family-community-hospital” four-level intelligent health management service model is proposed, the hardware architecture of intelligent healthcare management and the software maintenance system have been built. Through methods such as real-time multi-source data collection, mobile sensing, cloud computing, multi-network fusion technology, continuous monitoring and intelligent management of health data is realized convenient, fast and efficient. It solves the problems that the existing medical system cannot meet the multi-level health needs, personal data security and privacy protection, etc., it has achieved the goal of real-time interactive health management of regionalization, multi-level and multi-center, and whole-person, whole-process and all-round.

Design and development of a Wechat applet for intelligent health management of metabolic syndrome.

: To design and develop a Wechat applet for intelligent health management of metabolic syndrome. Based on the needs and requirements of individuals undergoing health check-up, patients with metabolic syndrome and medical workers, a Wechat applet for metabolic syndrome management was designed and developed, which involving health data collection, health risk prediction, health management knowledge base fusion and intelligent recommendation, data privacy and security. The platform consists of three user ports: individuals undergoing health check and patients with metabolic syndrome, the medical workers and the system administrators. The main functions of the platform included metabolic syndrome risk prediction, intelligent recommendation of health management strategies, health behavior record and supervision, experts' consultation and health knowledge guide. The Wechat applet developed in this study can be used for metabolic syndrome risk prediction for general population, and health management for patients with metabolic syndrome, which helps them to enhance health management awareness and health behavior adherence.

Combining Artificial Intelligence with Traditional Chinese Medicine for Intelligent Health Management

The growth of artificial intelligence (AI) is being referred to as the beginning of "the fourth industrial revolution". With the rapid development of hardware, algorithms, and applications, AI not only provides a new concept and relevant solutions to solve the problem of complexity science but also provides a new concept and method to promote the development of traditional Chinese medicine (TCM). In this study, based on the research and development of AI technology applications in biomedical and clinical diagnosis and treatment, we introduce AI technologies in current TCM research. This can have applications in intelligent clinical information acquisition, intelligent clinical decision, and efficacy evaluation of TCM; intelligent classification management, intelligent prescription, and drug research in Chinese herbal medicine; and health management. Furthermore, we propose a framework of "intelligent TCM" and outline its development prospects.

Study on the Types of Elderly Intelligent Health Management Technology and the Influencing Factors of Its Adoption.

[Background]: In recent years, aging has become a global social problem. Intelligent health management technology (IHMT) provides solutions for the elderly to deal with various health risks. However, the elderly are facing many difficulties in using IHMT. Studying the application types of IHMT and the influencing factors of the elderly’s acceptance of it will help to improve the use behavior of the elderly. [Methods]: This paper summarizes the application types of IHMT, identifies the influencing factors of the elderly’s adaption of IHMT, and makes a systematic comment on the influencing factors. [Results]: We divide the different functions of IHMT for the elderly into four types: self-monitoring, medical care, remote monitoring, and health education. The influencing factors are divided into three types: individual, social, and technology. [Conclusions]: This study finds that IHMT’s application covers all aspects of the health services of the elderly. Among these applications, self-monitoring is the most used. We divided the influencing factors of the elderly’s acceptance of IHMT into three categories and nine subcategories, having 25 variables.

A performance degradation assessment method using one-dimensional sparse representation self-learning dictionary

Rotating machinery is a widely-used component of factories and enterprises. Unquestionably, rolling bearing is the backbone of rotating machinery and its performance degradation assessment (PDA) is a vital part of condition-based intelligent health management. Therefore, a PDA method based on one-dimensional sparse representation self-learning dictionary is presented to assess the degradation trend of rolling-element bearings. We proposed a novel feature extraction method named one-dimensional K-singular value decomposition to extract the sensitive feature from run-to-failure data. Then, an improved support vector data description is adopted to establish the PDA model, and a novel degradation indicator is obtained. Subsequently, the proposed method adaptively tracks the development of deterioration and identifies the occurrence of an incipient fault. Experimental results on the simulation signal and XJTU-SY bearing datasets verify the effectiveness and outperform the conventional methods.