Health Status Monitoring System Research Articles

Analysis and Design of Wind Turbine Monitoring System Based on Edge Computing

INTRODUCTION: A wind turbine data analysis method based on the combination of Hadoop and edge computing is proposed. OBJECTIVES: Solve the wind turbine health status monitoring system large data, time extension, energy consumption and other problems. METHODS: By analysing the technical requirements and business processes of the system, the overall framework of the system was designed and a deep reinforcement learning algorithm based on big data was proposed. RESULTS: It solves the problem of insufficient computing resources as well as energy consumption and latency problems occurring in the data analysis layer, solves the problems in WTG task offloading, and improves the computational offloading efficiency of the edge nodes to complete the collection, storage, and analysis of WTG data. CONCLUSION: The data analysis and experimental simulation platform is built through Python, and the results show that the application of Hadoop and the edge computing offloading strategy based on the DDPG algorithm to the system improves the system's quality of service and computational performance, and the method is applicable to the distributed storage and analysis of the device in the massive monitoring data.

An IoT-Based Road Bridge Health Monitoring and Warning System.

Recent earthquakes worldwide have led to significant loss of life and structural damage to infrastructure, especially road bridges. Existing bridge monitoring systems have limitations, including restricted detection capabilities, subjectivity, human error, labor-intensive inspections, limited access to remote areas, and high costs. Aging infrastructures pose a critical concern for organizations and government funding policies, showing signs of decay and impending structural failure. To address these challenges, this research proposes an IoT-based bridge health status monitoring and warning system that is wireless, low-cost, durable, and user-friendly. The proposed system builds upon engineering standards and guidelines to classify bridge health status into categories ranging from excellent to collapse condition. It incorporates deflection, vibration, temperature, humidity, and infrared sensors, combined with IoT and a fuzzy logic algorithm. The primary objective is to reduce bridge maintenance costs, extend lifespans, and enhance transportation safety through an early warning system via a mobile application. Additionally, a Google Maps interface has been developed to display bridge conditions along with real-time traffic video. To validate the proposed system, a 3-D prototype model was constructed and tested. Practical testing of the fuzzy logic algorithm aligned with the simulation outcomes, demonstrating expected accuracy in determining bridge health status.

Progressive Classifier Mechanism for Bridge Expansion Joint Health Status Monitoring System Based on Acoustic Sensors.

The application of IoT (Internet of Things) technology to the health monitoring of expansion joints is of great importance in enhancing the efficiency of bridge expansion joint maintenance. In this study, a low-power, high-efficiency, end-to-cloud coordinated monitoring system analyzes acoustic signals to identify faults in bridge expansion joints. To address the issue of scarce authentic data related to bridge expansion joint failures, an expansion joint damage simulation data collection platform is established for well-annotated datasets. Based on this, a progressive two-level classifier mechanism is proposed, combining template matching based on AMPD (Automatic Peak Detection) and deep learning algorithms based on VMD (Variational Mode Decomposition), denoising, and utilizing edge and cloud computing power efficiently. The simulation-based datasets were used to test the two-level algorithm, with the first-level edge-end template matching algorithm achieving fault detection rates of 93.3% and the second-level cloud-based deep learning algorithm achieving classification accuracy of 98.4%. The proposed system in this paper has demonstrated efficient performance in monitoring the health of expansion joints, according to the aforementioned results.

AI-Based Low-Cost Real-Time Face Mask Detection and Health Status Monitoring System for COVID-19 Prevention

The outbreak of COVID-19 had brought a great challenge for the World Health Organization (WHO) in preventing the spreading of SARS-CoV-2. The Ministry of Health (MOH) of Malaysia introduced the MySejahtera mobile application for health monitoring and contact tracing. Wearing a face mask in public areas had been made compulsory by the Government. The overhead cost incurred in hiring the extra manpower to ensure all the visitors wear a face mask, check-in through MySejahtera and the status in MySejahtera is healthy before entering a premise. A low-cost solution is urgently needed to reduce the heavy overhead cost. An AI-Based Low-Cost Real-Time Face Mask Detection and Health Status Monitoring System (AFMHS) is proposed to perform real-time detection for the face mask and MySejahtera Check-In tickets by using artificial intelligence. MobileNetV2 was used for the detection and recognition of face and face masks. YOLOv3 was used for the detection of the region of interest for the MySejahtera Check-In ticket to locate the health and vaccination status of the visitor. Optical character recognition (OCR) is a technique that is used to detect the text captured in an image and encode the recognized text. OCR is implemented to recognize the text extracted from the ticket. Tesseract is used as the OCR engine in AFMHS. Raspberry-Pi-4B (Raspberry Pi Generation 4 Model B) with 4 GB RAM is used as the processing unit of AFMHS. The total cost of the AFMHS is only USD220. Extensive experimental tests were carried out to evaluate the performance of AFMHS. The optimum operation setup conditions are proposed to achieve 100% accuracy. The optimum operating distance for the face mask detector and MySejahtera Check-In ticket detector are 1.5m and 15cm respectively.

Sistem Monitoring Kondisi Kesehatan Sebelum dan Sesudah Olahraga Menggunakan Pulse Sensor dan Sensor DS18B20 dengan Metode Naive Bayes

Heart rate and body temperature are essential factors that must be considered before exercise is essential to optimize exercise and know the body's condition. The trainer conducts a health check, and management evaluates the exercise results conducted on the athlete. The Internet of Things is the solution provided to facilitate the monitoring of health conditions and measure exercise intensity. Before and after exercise, the health condition monitoring system is designed to determine the user's health condition by measuring heart rate and body temperature as parameters for decision-making by applying the Naïve Bayes method. The Naive Bayes method is rule-based with the python sci-kit-learn programming language and data communication with MQTT. From the study results, the Sports Health condition monitoring system has succeeded in providing monitoring results, condition prediction decisions, and the accuracy of the Naïve Bayes method by 75%.

Internet of Things-assisted intelligent monitoring model to analyze the physical health condition.

Physical health monitoring may take several forms, from individual quality changes to complex health checks carried out by health staff. Present health issues are detected with monitoring, and potential health problems are expected. Wearable sensors provide users with ease in everyday tracking, although many issues must be addressed in such sensor systems. The devices take a long time to obtain the requisite detection and diagnostic expertise and produce false alarms. In this paper, the Internet of Things-assisted Health Condition Monitoring system (IoT-HCMS) has been proposed to track and analyze the patient physical health condition. The proposed IoT-HCMS utilizes the intelligent monitoring model to follow the patient physical health day by day activities and instantaneously generate the health records. The system will indeed support patients in tracking psychological signs to minimize risks to their well-being. The experimental results show that the IoT-HCMS improves accuracy in patient health monitoring and has less response time.

Mobile information system for monitoring the spread of viruses in smart cities

The concept of creating a multi-level mobile personalized system for fighting viral diseases, in particular Covid-19, was developed. Using the integration of the Internet of Things, Cloud Computing and Big Data technologies, the system involves a combination of two architectures: client-server and publication-subscription. The advantage of the system is the permanent help with viral diseases, namely on communication, information, and medical stages. The smart city concept in the context of viral disease control focuses on the application of Big Data analysis methods and the improvement of forecasting procedures and emergency treatment protocols. Using different technologies, cloud server stores the positioning data obtained from different devices, and the application accesses API to display and analyze the positioning data in real time. Due to the technologies combination, internal and external positioning can be used with a certain accuracy degree, being useful for various medical and emergency situations and analysis and the following processing by other smart city information systems. The result of the given investigation is the development of the conceptual model of multi-level mobile personalized health status monitoring system used for intellectual data analysis, prediction, treatment and prevention of viral diseases such as Covid-19 in modern “smart city”.

Health status monitoring system based on speech analysis

Abstract In recent years, the health of the elderly has become the focus of medical treatment. In daily life, the physical condition of the elderly cannot be fed back to their families in time, and the health level of the elderly at home cannot be accurately judged. In order to solve the above problems, we put forward a method to monitor the health of the elderly with speech recognition system. This method can not only monitor the health of the elderly in real time, but also inform the family members in time when there is something wrong with their health. The specific steps are as follows: firstly, sound waves are converted into electrical signals by sound sensors. Because the frequency band distribution of the input sound wave signals is complex, we choose band-pass filters to shield other noises, and choose the sound frequency band belonging to specific elderly people as input signals. Because of the particularity of sound wave signals, they are time-varying signals, which need to be processed. We extract characteristic parameters and frame-by-frame stable digital signals to obtain stable signals for subsequent processing. The filter and signal processing chip are designed by FPGA, and the finished product is prepared after layout drawing. In the process of protecting the health of the elderly, the invention is equipped with image recognition, which is convenient for timely monitoring the living state of the elderly and whether there is sudden disease. Comprehensive speech recognition chip is used to protect the healthy life of the elderly. template matching Speech endpoint detection Speech database , Feature parameter extraction - - Template matching Judging whether the elderly are healthy or not Computational recognition Figure -Speech in training set (verification) Test set - : la oc Outside the training set Speech with noisy Voice (test) background Unrecognized speech Figure2

IEPE accelerometer fault diagnosis for maintenance management system information integration in a heavy industry

Abstract With the increasing demand for reliable production facilities, the design of a health condition monitoring system with the implementation of automatic diagnosis as well as software solutions is one of the main issues for a smart factory. Among many industrial applications, accelerometer is one of the most frequently used sensors for facility vibration monitoring. Thus, the health condition of the sensor itself is a critical factor for a correct diagnosis. Failure to monitor the sensor's health condition would potentially cause a false alarm, which may lead to a wrong decision making made by field operators. In this research, a preprocessing method of synthetic data and a Gaussian mixture model (GMM) classifier were developed to classify the health conditions of the online integrated electronic piezoelectric (IEPE) accelerometers. The proposed method was integrated into a product line and the test results achieved >99% of accuracy in determining five different health conditions of the accelerometers. With the aid of the proposed method, the time of human inspection can be significantly reduced and the field safety can also be improved. Moreover, false alarms caused by sensor failure can be prevented. This leads to increase in reliability of the facility monitoring system. 3 features to represent > 99% 3 features to represent > 99%

An Intelligent Harmonic Synthesis Technique for Air-Gap Eccentricity Fault Diagnosis in Induction Motors

Induction motors (IMs) are commonly used in various industrial applications. To improve energy consumption efficiency, a reliable IM health condition monitoring system is very useful to detect IM fault at its earliest stage to prevent operation degradation, and malfunction of IMs. An intelligent harmonic synthesis technique is proposed in this work to conduct incipient air-gap eccentricity fault detection in IMs. The fault harmonic series are synthesized to enhance fault features. Fault related local spectra are processed to derive fault indicators for IM air-gap eccentricity diagnosis. The effectiveness of the proposed harmonic synthesis technique is examined experimentally by IMs with static air-gap eccentricity and dynamic air-gap eccentricity states under different load conditions. Test results show that the developed harmonic synthesis technique can extract fault features effectively for initial IM air-gap eccentricity fault detection.

Health condition monitoring of insulated joints based on axle box acceleration measurements

This paper presents a health condition monitoring system for insulated rail joints (IRJs) based on axle box acceleration (ABA) measurements. The ABA signals from all the wheels of the measuring train are processed to extract those characteristics that better represent the quality of the IRJ. Then, different indicators are used for damage assessment, the most relevant being a set of frequency bands in the ABA power spectrum. A detection algorithm is proposed based on the derived frequency characteristics of the ABA signal. We compared the responses of IRJs in good condition with those in poor condition. Track inspections were performed to validate the health condition monitoring methodology in different IRJs of the Dutch railway network. The hit rate of IRJs detection was 84% for two validated tracks. The damage assessment procedure allowed to prioritize the IRJs that require maintenance. This information is useful for the railway inframanagers as it allows to predict where safety compromises will be faced.

An Enhanced Bispectrum Technique With Auxiliary Frequency Injection for Induction Motor Health Condition Monitoring

Induction motors (IMs) are widely used in various domestic and industrial applications. A reliable IM health condition monitoring system is very useful to detect IM fault at its incipient stage to prevent performance degradation of IMs and their driven equipment. An enhanced bispectrum (EB) technique with auxiliary frequency (AF) injection (EB-AF) is proposed in this paper to conduct incipient IM fault detection. The AFs are injected into the electric current signals to enhance fault characteristic frequency components. The EB technique transforms the traditional 2-D bispectrum with massive features into a one-scale spectral representation to facilitate feature extraction for IM defect detection. The effectiveness of the developed EB-AF technique is examined by IM bearing fault detection with different speed and load conditions. Test results show that the developed EB-AF technique can reveal fault features effectively for initial IM fault detection.

Thinking eHealth

This paper focuses on a mathematical background of an individual health status monitoring system to empower young people to manage their health. The proposed health status monitoring system uses symptoms observed with mobile sensing devices and prior information about health and environment (provided it exists) to define individual physical and psychological status. It assumes that a health status identification process is influenced by many parameters and conditions. It has a flexible logical inference system providing positive psychological influence on young people since full acceptance of recommendations on their behavioral changes towards healthy lifestyles is reached and a correct interpretation is guaranteed. The model and algorithms of the individual health status monitoring system are developed based on the composition inference rule in Zadeh's fuzzy logic. The model allows us to include in the algorithms of logical inference the possibility of masking (by means of a certain health condition) the symptoms of other health situations as well as prior information (if it exists) regarding health and environment. The algorithms are generated by optimizing the truth of a single natural “axiom”, which connects an individual health status (represented by classes of health situations) with symptoms and matrices of influence of health situations on symptoms and masking of symptoms. The new algorithms are fairly different from traditional algorithms, in which the result is produced in the course of numerous single processing rules. Therefore, the use of a composition inference rule makes a health status identification process faster and the obtained results more precise and efficient comparing to traditional algorithms.

Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel

Real time driver health condition monitoring system with drowsiness alertness was proposed. A new embedded electrocardiogram (ECG) sensor with electrically conductive fabric electrodes on the steering wheel of a car was designed to monitor the driver's health condition. The ECG signals were measured at a sampling rate of 100 Hz from the driver's palms as they stay on a pair of conductive fabric electrodes located on the steering wheel. Practical tests were conducted using an embedded ECG sensor with a wireless sensor node, and their performance was assessed under non-stop 2 h driving test. The ECG signals were measured and transmitted wirelessly to a base station connected to a server PC in personal area network environment. The driver's health condition such as the normal, fatigued and drowsy states was analysed by evaluating the heart rate variability in the time and frequency domains.

Highly sensitive driver health condition monitoring system using nonintrusive active electrodes

Abstract The proposed electrocardiogram (ECG) measurement system uses a nonintrusive ECG sensor with electrically active electrodes to measure an ECG signal without any noise effects. In this manner, the optimal position and size of the active electrodes attached to a car seat can be selected from several practical scenarios considering the physical position of the subject's heart to minimize the noise effects for the nonintrusive ECG measurement system in the car. The ECG signals from a clothed and seated occupant of the automobile are measured at a sampling rate of 100 Hz. A signal conditioning circuit was also designed to reduce the noise effects generated by the internal circuitry. The ECG signals are measured and transmitted wirelessly to a base station connected to a server PC in a personal area network for signal processing. The driver's condition is monitored wirelessly and analyzed by performing a heart rate variability analysis in the time and frequency domains.

Disease-First: A New Paradigm for Environmental Health Science Research

Disease-First: A New Paradigm for Environmental Health Science Research

Health, healthcare utilization and psychiatric disorder in people with intellectual disability in Taiwan

The aims of the present study were to examine health characteristics and healthcare utilization in relation to people with intellectual disability (ID) having psychiatric disorders in Taiwan. A cross-sectional study was employed; study subjects were recruited from the National Disability Registration Database. Taiwan, stratified by administrative geographical area for the study. Statistical analysis of 1026 carers for people with ID was made to examine the health status and healthcare utilization of individuals with ID having psychiatric disorders. Approximately 12.1% of people with ID had psychiatric disorders. These individuals were more likely to be poorer in health condition and consuming more medical services (in the outpatient, inpatient and emergency care areas), than those individuals without psychiatric disorders. These individuals with psychiatric disorders were also taking medicines regularly at a far greater percentage than did those without psychiatric disorders. Given the high prevalence of psychiatric disorders among individuals with ID, the healthcare system should take further steps to develop an appropriate health status monitoring system and community-based and easily accessible mental health services for them.