Smart Health Management Research Articles

Instance Segmentation and Automated Pig Posture Recognition for Smart Health Management

Instance Segmentation and Automated Pig Posture Recognition for Smart Health Management

Promoting precision health using fitness wearable and apps among breast cancer survivors: Protocols of a smart health management trial

BackgroundAnnually, approximately 1.7 million women are diagnosed with breast cancer worldwide. Engaging in regular physical activity (PA) post-diagnosis brings significant health benefits, enhancing breast cancer survivors' (BCS) prognosis and overall health-related quality of life (HRQoL). Despite these benefits, a low percentage of Chinese BCS adhere to the recommended moderate-to-vigorous PA levels. This highlights the need for innovative PA interventions tailored for BCS management. eHealth technology, such as fitness wearables and apps, presents an opportunity to improve BCS healthcare by offering personalized exercise programs. MethodsThis study focuses on developing a precision eHealth PA program for 200 Chinese BCS in Guangdong Province, using a custom micro-application and a smart band for a 12-month trial. All participants will receive a Huawei Smart Band and be divided into 1) a personalized intervention group, receiving daily PA tracking and feedback, and 2) a control group receiving standard care. The primary outcome is PA and secondary outcomes include biomarkers, weight and body composition, functional fitness, HRQoL, and individual beliefs. Outcomes will be assessed at baseline, 6 months, and 12 months (endpoint). Successful outcomes could revolutionize PA programs for Chinese BCS, providing a model for future eHealth interventions.

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.
 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.
 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.
 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.
 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.

Research on the Application of Smart Elderly Care Services in Home Elderly Care

The conventional wisdom about caring for the elderly inside families is crumbling under the weight of a fast-changing society and the mounting problems caused by an aging population. Smart senior care services leverage cutting-edge technology like AI, IoT, and big data analytics to provide comprehensive, specifically personalized care. This paper explores the theoretical foundations, practical applications, challenges, and recommended solutions for smart care services for the elderly using specific case studies to illustrate possible trends in future development. These services’ technical foundation consists of big data analysis to help identify the needs of the elderly, internet of things (IoT) to enable real-time monitoring, and artificial intelligence (AI) algorithms that offer smart health management solutions. These services include a wide range of tasks, such as monitoring health, helping with everyday tasks, and responding to emergencies. Costs, data security, and privacy protection are three of the many obstacles to marketing and expanding these services. A variety of approaches, including increased public awareness, supporting legislation, and regulatory actions, are proposed in the paper as potential solutions to these problems. Smart senior care services show great potential for home-based care, according to case studies. As time goes on and society evolves, these services are predicted to become more popular, making life easier and more pleasant for elders.

Real-time energy performance benchmarking of electric vehicle air conditioning systems using adaptive neural network and Gaussian process regression

One major concern of the electric vehicle is the limited driving range per charge. Among its auxiliary systems, the heating, ventilation, and air conditioning (HVAC) system consumes the largest amount of electricity and can have a significant impact on the driving range when operational faults occur. This paper proposes a real-time benchmarking method to continuously evaluate the energy performance of a large number of electric vehicle air conditioning systems. Each system is benchmarked based on the energy consumption of its peer systems. Considering the energy consumption is influenced by several impact factors including the ambient environment and the cooling capacity, this study encodes the impact factors with an Autoencoder to measure the similarities between operating conditions. Also, considering the difference in operating conditions between a system and its peers, the uncertainties in energy performance benchmarks of the peer systems are quantified by the Gaussian process given its probabilistic nature. For each peer system, a Gaussian process regression model is developed as a benchmark, and the performance of the target system is assessed by comparing its measured energy consumption with the averaged benchmarks of all its comparable peers, accounting for the uncertainties. With the continual learning algorithm adopted, the Autoencoder can be updated periodically to adapt to real-time operational data with minimal computational cost. The real-time benchmarking method is applied to electric bus air conditioners in Haikou and Sanya, China, and can effectively identify malfunctioning systems. This method can be conveniently deployed on cloud for smart health management of public electric vehicles in smart cities.

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.

Smart Health

In recent decades, healthcare organizations around the world have increasingly appreciated the value of information technologies for a variety of applications. Three of the new technological advancements that are impacting smart health are metaverse, artificial intelligence (AI), and data science. The metaverse is the intersection of three major technologies — AI, augmented reality (AR), and virtual reality (VR). Metaverse provides new possibilities and potential that are still emerging. The increased work efficiency enabled by artificial intelligence and data science in hospitals not only improves patient care but also cuts costs and workload for healthcare providers. Artificial intelligence, coupled with machine learning, is transforming the healthcare industry. The availability of big data enables data scientists to use the data for descriptive, predictive, and prescriptive analytics. This article reviews multiple case studies and the literature on AI and data science applications in hospital administration. The article also presents unresolved research questions and challenges in the applications of the metaverse, AI, and data science in the smart health context. For researchers, in addition to providing a good synopsis of the development and applications of the metaverse, AI, and data science in the healthcare area, this article identifies possible future research directions and discusses the possibilities of the metaverse, artificial intelligence, and data science in smart health. For practitioners, this article provides both hospital decision-makers and healthcare workers with practical guidelines and a smart health management model.

Effect of smartphone app-based health care intervention for health management of high-risk mothers: a study protocol for a randomized controlled trial

BackgroundThe 4th Industrial Revolution with the advent of the smart era, in which artificial intelligence, such as big data analysis and machine learning, is expected, and the provision of healthcare services using smartphones has become a reality. In particular, high-risk mothers who experience gestational diabetes, gestational hypertension, and prenatal and postpartum depression are highly likely to have adverse effects on the mother and newborn due to the disease. Therefore, continuous observation and intervention in health management are needed to prevent diseases and promote healthy behavior for a healthy life.MethodsThis randomized controlled trial will provide mothers 18 years of age or older with health care information collected based on evidence-based literature data using a smartphone app for 6 weeks. About 500 mothers will be selected in consideration of the dropout rate due to the characteristics of mothers. The study group and control group will be computer-generated in a 1:1 ratio through random assignment. The research group will receive health management items through the app, and health management information suitable for the pregnancy cycle is pushed to an alarm. The control group will receive the health management information of the paper. We also followed the procedure for developing mobile apps using the IDEAS framework.DiscussionThese results show the effectiveness of smart medical healthcare services and promote changes in health behaviors throughout pregnancy in high-risk mothers.Trial registrationClinical trial registration information for this study has been registered with WHO ICTRP and CRIS (Korea Clinical Research Information Service, CRIS). Clinical trial registration information is as follows:Study of development of integrated smart health management service for the whole life cycle of high-risk mothers and newborns based on community, KCT0007193. Registered on April 14, 2022, prospectively registered. This protocol version is Version 1.0. April 14, 2022.

Smart teledentistry healthcare architecture for medical big data analysis using IoT-enabled environment

The current spread out in Big Data analytics and the medical Internet of Things (IoT) originated the recognition of smart health. Smart health is the integration of devices, sensors, cameras, and objects or things embedded with sensors that generate an enormous amount of data known as big data. IoT-enabled smart health management assimilates the digital traces generated by things and humans including teledentistry. The key purpose of teledentistry is to analyze the data and produce valuable insights and hidden patterns to offer services and overcome the existing challenges. This article proposes a data management design using Big Data analytics for smart teledentistry planning. The proposed scheme is the customization of the present parallel platforms to accomplish effective processing. The design offers three different modules which are the data pre-processing, data ingestion, and data processing module. The pre-processing is performed to speed up the real data processing. The recent schemes lack well-organized and effective parallel data loading and proficient communication. The Big Data storage and ingestion are achieved using optimized utility and parallel mechanisms. Optimized RDD-enabled (resilient distribution) Yet Another Resource Negotiator (YARN) based proposal is designed for resourceful cluster administration and computation of medical Big Data. The proposed data design is experimentally simulated with authentic datasets using Apache Spark 3.0 framework. The proposed architecture is compared with existing state-of-the-art proposals. The simulation results reveal the efficacy of the proposed system.

Impact of Using the Intelligent Physical Health Measurement System on Active Aging: A Survey in Taiwan.

Background: In Taiwan, the Chiayi City Government and Industrial Development Bureau of the Ministry of Economic Affairs have worked together to promote smart health management in the community and encourage people to use the intelligent physical health measurement system (IPHMS) with Smart Body Health Measuring Machine. Volunteers help participants in the community to use the IPHMS to ensure that measurements are taken correctly. Objectives: This study aimed to explore volunteers’ satisfaction with using the IPHMS and the effects of the measurement service on the participants’ measurement behavior intention, and further explore the impact on their active aging. Methods: This study used a paper questionnaire to survey both the participants of the measurement service and the community volunteers from March to April 2021. A total of 180 valid responses were collected. Results: The sociodemographic information showed that the volunteers were mostly female, were aged over 61 years old, had received junior college education, had spent less than 3–6 years in community service, and had 6 months to 1 year of measured service experience. Additionally, the participants of the measurement service were mostly female, were aged over 61 years old, had received below middle school education, had spent less than 1–3 years in community service, and spent an average of 5 days in the community each week. Our results showed that the information quality (β = 0.352, p < 0.001) and system quality (β = 0.701, p < 0.001) had significant effects on volunteers’ satisfaction of using the IPHMS. Subjective norms had significant effects on participants’ perceived disease threat (β = 0.347, p < 0.001) and behavior intention of management service (β = 0.701, p < 0.001); furthermore, behavior intention had significant effects on their social participation for active aging (β = 0.430, p < 0.05). Conclusions: Improving the system and information quality is likely to improve volunteers’ satisfaction with the system. Active aging factors only affect social participation, which represents the measurement services promote for social interaction mostly.

IoT privacy preservation using blockchain

ABSTRACT Internet of Things (IoT) security is a fundamental challenge for the enterprises working behind the buildup of the IoT, and it is related applications. In today’s ara, with IoT, another buzzword is a blockchain-based cryptocurrency and other blockchain applications. Blockchain technology has proven itself one of the most secure existing technology in trust and privacy. This paper discusses IoT and blockchain technology fundamentals, followed by a discussion regarding numerous consensus models. We also discuss mathematical foundations used by blockchain technology, and it is followed by the proposed privacy-preserving technique for IoT devices with better device identity management using blockchain technology. This paper presents a novel solution for privacy preservation and identity allocation using secure identity management through public key-based cryptography-based blockchain. Though we consider smart home management and smart health management’s ecosystem for applicability discussion of the proposed scheme, the proposed system can be applied to any IoT application for better privacy preservation. Finally, we conclude with the discussion regarding the futuristic scope of the proposed system.//

Constrained Markov Decision Process Modeling for Optimal Sensing of Cardiac Events in Mobile Health

Rapid advances in the smartphone, wearable sensing, and wireless communication provide an unprecedented opportunity to develop mobile systems for smart health management. Mobile cardiac sensing collects health-related data from individuals and enables the extraction of information pertinent to cardiac conditions. However, wireless sensors in ambulatory care settings operate on batteries. All-time sensing and monitoring will result in fast depletion of the battery in the mobile system. There is an urgent need to develop optimal sensing schemes that will reduce energy consumption while satisfying the requirements in the detection of cardiac events. In this article, we develop a constrained Markov decision process (CMDP) framework to optimize mobile electrocardiography (ECG) sensing under the constraint of the energy budget. We first characterize the cardiac states from ECG signals using the heterogeneous recurrence analysis. Second, we model the stochastic dynamics in cardiac processes as a continuous-time Markov chain (CTMC). Third, we optimize the ECG sensing through a CMDP framework under the constraint of energy budget. Finally, we validate and evaluate the performance of our CMDP policy in both simulation and real-world case studies. Experimental results demonstrate that the proposed CMDP policy significantly outperforms the traditional uniform and mean-time-to-event (MTTE) policies. Specifically, the error of state estimation is reduced by 34.0% in the real-world case study for energy-constrained sensing of cardiac events. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the markedly increasing applications of mobile health (mHealth) in cardiac care. MHealth systems enable the real-time monitoring, tracking, and transmitting of heart-health information but suffer from the problem with limited battery life. This article presents a novel energy-efficient framework for mobile ECG sensing by integrating ECG signal analysis with sensing-policy optimization under the constraint of the energy budget. Experimental results demonstrate the effectiveness of the proposed framework to realize energy-efficient mobile sensing for cardiac events detection.

Smart Health Monitoring and Management Using Internet of Things, Artificial Intelligence with Cloud Based Processing

: Smart health monitoring system is a system that shortens the distance between a patient and the relevant medical organization. These systems have rapidly evolved during the past two decades and have the potential to change the way health cases are currently delivered. The Internet of Things (IoT) is an innovation for smart health management. It provides monitoring patients remotely and guarantees giving patients the medication and getting complete health care without the latter getting infected. As we know that the NovelCorona-virus also known as covid-19 expanded its impacts from China and still expands its catchment, national as well as international measures are being taken to contain the outbreak such as the placing of lockdown in nations. As a result, many people are being infected making the hospital incapable of providing proper healthcare. This paper proposes a smart health system that monitors the patients holding the coronavirus remotely and to protect the lives of the health service members (like physicians, nurses) from infection. This smart system observes patients by using sensors, to gather rich information every minute seconds. This benefits the patient as well as the service members because the physicians can observe the patient while freeing up beds in the hospitals for the critical cases.

Smart Health Management Framework for Maritime Communication System

Chen, B. and Li, J., 2020. Smart health management framework for maritime communication system. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 286–290. Coconut Creek (Florida), ISSN 0749-0208.Health management engineering can achieve health assessment, prediction and maintenance for maritime communication system to give power maritime communication ability. Basing system health management theory, health management architecture consisting of data acquisition, health assessment, fault diagnosis, intelligent decision and interface system is set up and then a comprehensive with whole-process and all-members health engineering is illustrated. Introducing cloud computing and Internet of things, smart health management is presented. Taking Internet of things as fundamental structure and cloud computing as platform architecture, the frame of smart health management for maritime communication system is formed. Thus, the smart health management framework is built with hierarchical application structure according to health requirement and business of maritime communication system. Two parts, cloud client and cloud platform are involved in the framework. Cloud client is both the user and provider of health services while cloud platform can manage and schedule cloud resources uniformity. An application instance with typical case is present last to explain the framework built. The purpose of the research in this paper is to translate technology and resources in smart health management system into maritime Communication benefits.

Network modeling and Internet of things for smart and connected health systems—a case study for smart heart health monitoring and management

Heart disease is a leading cause of death in the US. Recent advances in the Internet of Things (IoT) provide a great opportunity to realize smart and connected health systems through IoT monitoring and sensor-based data analytics of cardiac disorders. However, big data arising from the large-scale IoT system pose a significant challenge for efficient and effective sensory information processing and decision making. Very little has been done to glean pertinent information about the disease-altered cardiac activity in the context of large-scale IoT network. In this study, we propose a parallel computing framework for multi-level network modeling and monitoring of cardiac dynamics to realize the potential of IoT-enabled smart health management. Specifically, dissimilarities among cardiac signals are firstly characterized among heartbeats for an individual patient, as well as among representative heartbeats for different patients. Then, a stochastic learning approach is developed to optimize the embedding of cardiac signals into a beat-to-beat network model, as well as a patient-to-patient network model. Further, we develop a parallel computing algorithm to improve the computational efficiency. Finally, a statistical process monitoring scheme is designed to harness network features for real-time monitoring and anomaly detection of cardiac activities. Experimental results show the proposed methodology has strong potential to realize a smart and interconnected system for cardiac health management in the context of large-scale IoT network.

Smart Health Management with Ancient Wisdom

Health management is an important global issue in the light of aging population and prevailing chronic illnesses. Other than trauma and accidents, many illnesses typically evolve over a long period of time from healthy to sub-optima health and eventually seriously ill state. Many of these cases slowly becoming chronic and have to rely on conventional medicines to control symptoms over along period of time. Others developed into acute and critical cases requiring costly and severe measures such as surgeries, radio and chemo therapies thus subjecting patients to increasing risk of side effects and longer period of rehabilitation.

Smartphone-Based Blood Lipid Data Acquisition for Cardiovascular Disease Management in Internet of Medical Things

With the ubiquitous deployment of smartphones worldwide recently, biomedical diagnostic devices based on smartphones are potential to be widely used for smart health management on the Internet of Medical Things (IoMT) applications under point-of-care (PoC) settings. Therefore, this paper introduces one smartphone-based blood lipid data acquisition dongle to monitor blood lipid level, including total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C), using only a fingertip blood drop for cardiovascular disease management. During detection, a thin photochemical test strip that consists of LEDs and detectors is plugged into the compact dongle, where changed color intensities will indicate the lipid determinant concentration from the measured reflection coefficient of the chromogenic product. Such smartphone-based photochemical blood lipid data acquisition dongle was validated by comparing with a clinical analyzer, and reached a correlation coefficient over 0.903 and averaged coefficient of variation (CV)% of 4.575% for blood lipid measurement of 115 patients. The photochemical dongle is, therefore, promising for the future chronic disease management in the IoMT.

Implementation of Unconstrained Chair-Type Smart Health Management Monitoring System

Modern people spend many hours of daily life, such as study and work, to the sitting position with chairs and sofas. If you sit for a long time with a wrong posture, musculoskeletal system disorder can occur and distraction due to low concentrations. In this research, we implemented the chair-type smart health management monitoring system which provides the user with the traditional functions of the chair more effectively and also enables various health management functions. For this reason Three load cells (SB S - beam load cell, 100 kgf) were arranged in an equilateral triangle shape between the top plate and the lower plate of the chair to construct a control section. In addition, we designed a measuring section composed of a filter and an amplifier so that we can simultaneously measure weight signal and BCG. We implemented an information-based posture discrimination algorithm of weight information and a distraction estimation method and implemented a PC and Android-based smartphone monitoring system. We conducted two experiments to evaluate the performance of the implemented system. First of all, in order to evaluate the performance of the posture discrimination algorithm, nine postures were obtained for 10 test subjects in an arbitrary order. As a result of comparing arbitrary posture and posture discrimination results, it showed discrimination performance of 97.9%. Finally, experiment was conducted to confirm the usefulness of the discrimination of distraction in daily life. The experimenter checked the change of the indicator according to the posture change during audiovisual data appreciation. Distraction continued changing posture as a result of confirming the image of the section where the numerical value is high, and confirmed that it is not concentrating on the image. The implement system can help not only health care function in daily life, but also to induce proper posture and improve sitting habit. In future studies, we intend to conduct a research to objectively demonstrate the usefulness of the Distraction estimated index.

Smart health management with ancient wisdom

Smart health management with ancient wisdom

From Genome Sequencing to Bedside

To summarize excellent current research in the field of Bioinformatics and Translational Informatics with application in the health domain and evidence-based medicine. We provide a synopsis of the articles selected for the IMIA Yearbook 2013, from which we attempt to derive a synthetic overview of current and future activities in the field. Three steps of selection were performed by querying PubMed and Web of Science. A first set of 5,549 articles was refined into a second set of 1,272 articles from which 15 articles were retained for peer-review. The selection and evaluation process of this Yearbook's section on Bioinformatics and Translational Informatics yielded four excellent articles regarding the Human Genome and Medicine. Exploiting genomic data depends on having the appropriate reference annotation available. In the first article, the goal of the GENCODE Consortium is to produce and publish The GENCODE human reference gene set. As a result it is composed by merged manual and automatic annotations, which are frequently updated from public experimental databases. The quality of genome sequencing is platform-dependant. In the second article, a generic database independent from the sequencing technologies, Huvariome, can help to identify errors and inconsistencies in sequencing. To understand complex diseases of patients it will be of great importance to detect rare gene variants. This is the aim of the third study. Finally, in the last article, the plasma's DNA of healthy individual and patients suffering from cancer is compared. The current research activities attest to the continuous convergence of Bioinformatics and Medical Informatics for clinical practice. For instance, a direct use of high throughput sequencing technologies for patients could aid the diagnosis of complex diseases (such as cancer) without invasive surgery (such as biopsy) but only with blood analysis. However, ongoing genomic tests will generate massive amounts of data and will imply new trends in the near future: "Big Data" and smart health management.