Efficient Health Monitoring System Research Articles

Integrated health monitoring system for checking patients’ vitals

The increasing demand for efficient healthcare services has propelled the development of efficient health monitoring systems. This paper presents a comprehensive system designed to monitor the vital signs of patients in real-time, aimed at enhancing patient care and reducing the burden on healthcare providers. The system integrates various sensors to Data collected from these sensors are transmitted wirelessly to a central database, where it is processed and analyses using sophisticated algorithms. The results are accessible through a user-friendly interface for both healthcare professionals and patients. This health monitoring system offers several critical benefits. it provides real-time alerts for any abnormal vital signs, enabling prompt medical intervention. Secondly, the continuous monitoring allows for the early detection of potential health issues, which can significantly improve patient outcomes. The methodology involves is the analytical and systematic application of Arduino Nano Boards as microcontrollers, such as DS18B20, DHT11 and with WIFI Modula; which are driven- real time-based temperature frameworks for optimization and abstraction of accurate patient’s health data result. Results demonstrated the system's accuracy, reliability, and user satisfaction.

An IMU-based machine learning approach for daily behavior pattern recognition in dairy cows

Technological advancements have revolutionized livestock farming, notably in health monitoring. Traditional methods, which have been criticized for subjectivity and treatment delays, can be replaced with efficient health monitoring systems, thereby reducing costs and workload. Implementing cow behavior recognition allows for effective dairy cow health monitoring. In this research, we propose an integrated system using inertial measurement unit (IMU) devices and machine learning techniques for dairy cow behavior recognition. Six main dairy cow behaviors were studied: lying, standing, walking, drinking, feeding, and ruminating. All behavior types were manually labeled into the IMU data by reviewing the recorded footage. The labeled IMU data underwent four processing steps: selecting different window sizes, feature extraction, feature selection, and normalization. These processed data were then used to build the behavior recognition model. Various model structures, including SVM, Random Forest, and XGBoost, were tested. The top-performing model, XGBoost, with its proposed 58 features achieved an F1-score of 0.87, with specific scores of 0.93 for lying, 0.85 for walking, 0.94 for ruminating, 0.89 for feeding, 0.86 for standing, 0.93 for drinking, and 0.59 for other activities. During our online testing, we observed similar patterns for each healthy cow. The cumulative time spent on each behavior also matched the statistics from previous surveys. Additionally, our backend optimization approach resulted in a final overall percentage error of 1.55 % per day during online testing. In conclusion, our study presents an IMU-based system capable of accurately recognizing dairy cow behavior. Feature design and appropriate models are proposed herein. A functional optimization method was introduced indicating that our system has the potential with applications for estrus detection and other reproductive management practices in the dairy industry.

An FPGA Implementation of Health Monitoring System using IOT

Abstract: The objective of this study is to establish a highly efficient health monitoring system that focuses on key parameters such as heart rate and temperature. To achieve this, the proposed system utilizes FPGA technology for rapid information processing. Health data, collected through a Pulse sensor and Temperature sensor, is transmitted using an ADC. The system continuously monitors the heart rate and temperature of the patient's body. For instance, the heart rate is measured using a Pulse sensor, while the Temperature sensor detects the body temperature. The encoded serial data is then transmitted through a transmitting module, which is connected to a patient's location. The receiver unit can be an LCD display at the patient's place, or it can be a laptop, desktop, or mobile phone connected to the internet. The receiver can be either a doctor or a person close to the patient. A transmitter unit near the patient sends the data through an FPGA and a Wi-Fi module, which is connected to it, receives and updates the data on a server. The data is continuously displayed on a user interface visible on a PC or laptop using the IP address. This allows doctors or responsible individuals to observe and monitor multiple patients simultaneously. The system also continuously monitors the patient's data, displaying it near the patient and updating it on the server. By using the IP address of a specific patient, doctors can easily assess the patient's condition. The receiver, an LCD display at the patient's location, and another connected device (laptop, desktop, or mobile phone) constantly update and display the data. The system enables remote monitoring and real-time observation by doctors or caregivers. The research highlights the accessibility of FPGA-based design resources

Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier

In the industry, continuous health monitoring of electric motors is considered as an essential requirement. The continuous operation of the electric motor may cause malfunctions and addressing them timely is a critical challenge. The development of an efficient health monitoring system based on the identification of electrical motor faults is on great demand. This paper addresses the fault detection technique using discrete wavelet transform (DWT) algorithm for continuous health monitoring of electric motor-based systems. The faults have been detected through Motor Current Signature Analysis (MCSA) series procedures using the proposed method. Concurrently, the wavelet transform algorithm produces frequency-based spectrum related to the stator current parameters to accomplish the fault classification. This study provides an analysis of three motor faults of Phase imbalance, Rotor misalignment, and High contact resistance (HCR). DWT has the ability to categorize the input signals into approximate coefficient state for low frequency signals and detailed coefficient state for high frequency signals. In this research, this technique is used to detect faults because it is able of processing signals of very low frequency, and effectively deal with intermittent sharp signals that appear frequently during processing. DWT technique based on conditional monitoring of an induction motor with precise detailed coefficients and more skilled at light loads given on a motor-shaft with relatively fast execution time compared to FFT. Furthermore, the comparison of healthy and faulty induction motors has been compiled by Linear Discriminant Analysis (LDA) technique, a sub-application of MATLAB, and used for faults management purposes. LDA in comparison with PCA gives more perfect results. In this research, different faults have been detected with 100% accuracy using LDA classifier. The implementation of the proposed scheme will be beneficial in avoiding faults by ensuring that preemptive measures are taken timely against these faults, and the production of industries is protected from revenue losses.

Agent-Based Medical Health Monitoring System

One of the leading healthcare concerns worldwide is the aging population. Aged patients require more significant healthcare resources because they are more likely to have chronic diseases that result in higher healthcare expenses. The design and implementation of e-health solutions, which offer patients mobile services to assist and enhance their treatment based on monitoring specific physiological data, is one of the key achievements in medical information technology. In the last few decades, there have been tremendous advancements in healthcare technology regarding mobility, size, speed, and communication. However, the critical drawback of today’s e-Health monitoring systems is that patients are confined to smart rooms and beds with monitoring gadgets. Such tracking is not widespread due to chronic patients’ mobility, privacy, and flexibility issues. Further, health monitoring devices that are fastened to a patient’s body do not give any analysis or advice. To improve the health monitoring process, a multi-agent-based system for health monitoring is provided in this study, which entails a group of intelligent agents that gather patient data, reason together, and propose actions to patients and medical professionals in a mobile context. A multi-agent-based framework presented in this study is evaluated through a case study. The results show that the proposed system provides an efficient health monitoring system for chronic, aged, and remote patients. Further, the proposed approach outperforms the existing mHealth system, allowing for timely health facilities for remote patients using 5G technology.

Long Term Application of a Vehicle-Based Health Monitoring System to Short and Medium Span Bridges and Damage Detection Sensitivity

Largest portion of the bridge stock in almost any country and bridge owning organisation consists on ordinary bridges that has short or medium spans and are now deteriorating due to aging, etc. Therefore, it is becoming an important social concern to develop and put to practical use simple and efficient health monitoring systems for existing short and medium span (10 - 30 m) bridges. In this paper, one practical solution to the problem for condition assessment of short and medium span bridges was discussed. A vehicle-based measurement with a public bus as part of a public transit system (called “Bus monitoring system”) has been developed to be capable of detecting damage that may affect the structural safety of a bridge from long term vibration measurement data collected while the vehicle (bus) crossed the target bridges. This paper systematically describes how the system has been developed. The bus monitoring system aims to detect the transition from the damage acceleration period, in which the structural safety of an aged bridge declines sharply, to the deterioration period by continually monitoring the bridge of interest. To evaluate the practicality of the newly developed bus monitoring system, it has been field-tested over a period of about four years by using an in-service fixed-route bus operating on a bus route in the city of Ube, Yamaguchi Prefecture, Japan. The verification results thus obtained are also described in this paper. This study also evaluates the sensitivity of “characteristic deflection”, which is a bridge (health) condition indicator used by the bus monitoring system, in damage detection. Sensitivity of “characteristic deflection” is verified by introducing artificial damage into a bridge that has ended its service life and is awaiting removal. As the results, it will be able to make a rational long-term health monitoring system for existing short and mediumspan bridges, and then the system helps bridge administrators to establish the rational maintenance strategies.

INFANT MONITORING SYSTEM USING MULTIPLE SENSORS

This paper proposes an efficient health monitoring system for infants, using wearable sensor systems with wireless communication. We have developed a system including fully integrated sensors for measuring ECG, temperature and CO2 level around the infant’s crib. This system can provide continuous monitoring of the infant’s vital parameters and can be used in Neonatal Intensive Care Units (NICU) and at home.

An Efficient and Reliable Structural Health Monitoring System for Buildings after Earthquake

Occupation of buildings of high importance like hospitals and shelters after earthquake is a risky yet vital task for rescue workers. This paper presents a structural health monitoring system to ensure the safety and reliability of the buildings after earth quack. It is understood that ground motion and lateral displacement due to earthquack may cause deformation and thus excessive strain and stress at the main structural elements. Therefore, the building may suddenly goes to failure, requiring a reliable yet efficient health monitoring system. An array of piezoelectric sensors is mounted at desired location to measure the deformation and stress at critical points. The voltage generated by piezoelectric sensors is sent to computer via a data acquisition system. Measuring and monitoring the trend of changing sensors voltages indicate the probability of existing damages and the rate of propagation. The performance-based seismic is reported based on the nonlinear static analysis (pushover) under the influence of the lateral loading and structural behaviour through the Sap2000® software and FEMA356. The proposed model is verified for a three-story steel structure building. The effects of the lateral displacement caused by earthquake forces on strain and sensors voltage are investigated for each main element in each floor. Increasing the strain and displacements at selected elements increases the voltage generated at piezoelectric sensors. Continuous monitoring and analysis of generated signals helps the building manager to apply warning alarm or call for evacuation of the building.

Numerical simulation of the Lamb wave propagation in honeycomb sandwich panels: A parametric study

The paper aims to describe the guided Lamb wave propagation in honeycomb sandwich panels. The application of Lamb waves is a well-known method in modern online structural health monitoring techniques. To analyze the wave propagation in such a complicated geometry with an analytical solution is hardly possible; therefore a dimensional finite element simulation is used. A piezoelectric actuator is used to excite the waves on the sandwich panel surface. The extended model of the honeycomb sandwich panel is consisting of two plate layers and a mid-core layer. In addition, a simplified model is used to reduce the computing costs, where the mid-core of the sandwich panel is replaced by a homogeneous layer. The results from the extended model and the simplified model are in most cases in a good agreement; however the limitations of using the simplified model are discussed. A parametric study is used to show the influence of the geometrical properties of honeycomb plates, the material properties of the skin plates and the loading frequency on the group velocity, the wave length and the energy transmission. Each of these properties provides valuable information to design an efficient health monitoring system. Finally, an experimental test is presented.

Monitoring Patients' Signs Wirelessly

Recent developments in off-the-shelf wireless embedded computing boards and the increasing need for efficient health monitoring systems, fueled by the increasing number of patients, has prompted R&D professionals to explore better health monitoring systems that are both mobile and cheap. This work investigates the feasibility of using the ZigBee embedded technology in health-related monitoring applications. Selected vital signs of patients are acquired using sensor nodes and readings are transmitted wirelessly using devices that utilize the ZigBee communications protocols. A prototype system has been developed and tested with encouraging results.

Structural assessment under uncertain parameters via interval analysis

An efficient health monitoring system for damage detection in civil engineering structures using on-line monitoring data is being developed to identify any possible damage in short time. The present work is based on the treatment of uncertainties, which is one of the basic common difficulties faced when modelling structures. A methodology, based on interval analysis (IA) theory [R.E. Moore, Interval Analysis, Prentice-Hall, Englewood Cliffs, NJ, 1966] applied to a numerical constraint satisfaction problem (CSP) [J.R. Casas, J.C. Matos, J.A. Figueiras, J. Vehí, O. García, P. Herrero, Bridge monitoring and assessment under uncertainty via interval analysis, in: Ninth International Conference On Structural Safety And Reliability—ICOSSAR2005, 2005. pp. 487–494], is implemented in the damage detection [J.R. Casas, J.C. Matos, J.A. Figueiras, J. Vehí, O. García, P. Herrero, Bridge monitoring and assessment under uncertainty via interval analysis, in: Ninth International Conference On Structural Safety And Reliability—ICOSSAR2005, 2005. pp. 487–494] and modelling system of a long-term monitoring project in order to achieve such an objective. An algorithm is being developed for using such methodology with the obtained data. Such methodology has been first checked in the laboratory with a simple reinforced concrete structure (loaded up to failure). The obtained results are useful for identifying the load where the structure presents changes in its behaviour. The majority of the structures present a linear elastic behaviour throughout their life. However, they tend to deteriorate; such degradation reflects on results obtained from the long term monitoring system. Structural assessment was successfully performed in this case, enabling its application to real structures.