Patient Health Monitoring System Research Articles

Wireless Patient Health Monitoring System

Wireless Patient Health Monitoring Systems (WPHMS) have emerged as transformative solutions in modern healthcare, facilitating continuous and remote monitoring of patients' vital signs in real-time. This paper presents a comprehensive exploration of a Wireless Patient Health Monitoring System leveraging the Blynk app, a versatile IoT platform. The system is designed to enable seamless data collection, transmission, and visualization of critical health parameters such as pulse rate, temperature, and blood pressure. By integrating cutting-edge hardware components with intuitive software interfaces, the proposed system offers unprecedented flexibility and accessibility in patient monitoring, transcending the limitations of traditional wired monitoring systems. The research begins with a thorough review of existing literature on wireless healthcare technologies, highlighting the evolution of patient monitoring systems and the role of IoT platforms in revolutionizing healthcare delivery. Building upon this foundation, the paper proceeds to outline the hardware architecture of the WPHMS, detailing the selection and integration of microcontroller boards, sensor modules.

Continuous Health Monitoring System For Patients Using IoT

Internet of Things (IoT) can be used to monitor health conditions of patients after major surgeries. Information from various sensors is processed by PIC16F877A microcontroller which sends data to GSM Network with the help of IoT module. A relay is used to switch between IoT module and GSM module based on the output voltage of the sensors. The data transmitted from the microcontroller is stored on the cloud for long term storage and access. The address of the webpage used should be configured in the IoT module. Alert signal is sent to caretaker in case of emergency. This system is easy to setup with low power consumption and high performance. This system uses GSM network so can be used even in rural areas where broadband facility is not available.

Heart patient health monitoring system using invasive and non-invasive measurement

The abnormal heart conduction, known as arrhythmia, can contribute to cardiac diseases that carry the risk of fatal consequences. Healthcare professionals typically use electrocardiogram (ECG) signals and certain preliminary tests to identify abnormal patterns in a patient’s cardiac activity. To assess the overall cardiac health condition, cardiac specialists monitor these activities separately. This procedure may be arduous and time-intensive, potentially impacting the patient’s well-being. This study automates and introduces a novel solution for predicting the cardiac health conditions, specifically identifying cardiac morbidity and arrhythmia in patients by using invasive and non-invasive measurements. The experimental analyses conducted in medical studies entail extremely sensitive data and any partial or biased diagnoses in this field are deemed unacceptable. Therefore, this research aims to introduce a new concept of determining the uncertainty level of machine learning algorithms using information entropy. To assess the effectiveness of machine learning algorithms information entropy can be considered as a unique performance evaluator of the machine learning algorithm which is not selected previously any studies within the realm of bio-computational research. This experiment was conducted on arrhythmia and heart disease datasets collected from Massachusetts Institute of Technology-Berth Israel Hospital-arrhythmia (DB-1) and Cleveland Heart Disease (DB-2), respectively. Our framework consists of four significant steps: 1) Data acquisition, 2) Feature preprocessing approach, 3) Implementation of learning algorithms, and 4) Information Entropy. The results demonstrate the average performance in terms of accuracy achieved by the classification algorithms: Neural Network (NN) achieved 99.74%, K-Nearest Neighbor (KNN) 98.98%, Support Vector Machine (SVM) 99.37%, Random Forest (RF) 99.76 % and Naïve Bayes (NB) 98.66% respectively. We believe that this study paves the way for further research, offering a framework for identifying cardiac health conditions through machine learning techniques.

Design and Implementation of an IoT Based Patient's Health Monitoring System

This paper presents an Internet of Things (IoT) based patient's health monitoring system that can be given to patients, especially in remote areas from the hospitals. This system is built around Node MCU (ESP8266) which is used to control the activities of the system. The microcontroller is interfaced to MLX90614 which is the contactless infrared (IR) temperature sensor and MAX30101 which is the High-Sensitivity Pulse Oximeter and Heart Rate Sensor. The software implementation involves the programming of the microcontroller in Arduino IDE environment and the setting up of the Blynk cloud platform for receiving data from the system. This data can be accessed remotely in the Blynk platform through internet connection. The results obtained show that the system performed optimally as the percentage error margin in readings obtained from the system for body temperature, heart rate (pulse rate), and blood Oxygen level when compared with commercially available health measuring devices show a maximum percentage error of 3.51 in heart rate and a minimum percentage error of - 3.42 in body temperature. Again, the Root Mean Square Error (RMSE) for body temperature, heart rate (pulse rate), and blood Oxygen level when compared with commercially available health measuring devices are 0.351, 0.935, and 2.503 respectively. These values are less than the 2% stipulated by the US Food and Drug Administration (FDA) requirement of root mean square accuracy for pulse oximeters, and the percentage error values in this work are less than ±5% for oximeter. This means that the system is reliable.

A Review on Real Time Patient Health Monitoring System Using Cloud Technology

Abstract: The rising demand for advanced healthcare solutions has driven the development of innovative technologies aimed at improving patient care. This project introduces a Real-Time Patient Health Monitoring System, which utilizes cloud technology to its full potential. By incorporating cloud infrastructure, data science, and real-time analytics, the system offers continuous monitoring and analysis of crucial health metrics such as SpO2, temperature, and pulse rate. Designed with wearability in mind, the system's architecture ensures uninterrupted monitoring and instant feedback for both patients and healthcare professionals. Cloud technology enables seamless data storage, retrieval, and real-time analysis, thereby providing comprehensive insights into a patient's health status. The project's scalability guarantees its suitability for various healthcare environments, rendering it a versatile tool for hospitals, clinics, and remote healthcare scenarios.

Patient Reminder System

Overview Patient Health Monitoring on ESP8266 Healthcare technology is very popular in this pandemic situation because of coronavirus. Actually, health care technology is rapidly being revolutionized with the help of the Internet of Things (IoT). Monitoring the health status of a covid patient is a hard task because of our busy schedule and our daily work. Mostly, the elderly covid patients should be monitored periodically. So, I thought to make an innovative system in this lockdown to automate the task. This device uses an ESP8266 webserver to track patient health using this monitoring system. Hence, patient health parameters such as body temperature, heart rate (BPM), blood oxygen levels (Sp02) as well as room temperature and humidity can be monitored from any device (like Smartphone, PC, Laptop, Smart TV,.) That support browsing capabilities. In this project, we will learn how to build an ESP8266 based Patient Health Monitoring System. To measure Heart Rate/Pulse (BPM) and Blood Oxygen Level (SpO2), we use the MAX30100 pulse oximeter sensor. Similarly, to measure body temperature, we use the DS18B20 temperature sensor. Meanwhile, the patient is inside the room. So we need to monitor room temperature and humidity level as well. We should keep them in a room with a certain temperature and humidity level to not feel uncomfortable. Hence, we use the DHT22 Temperature & Humidity sensor. A Smart Patient Health Monitoring System using IoT (Internet of Things) is a technology-driven solution designed to continuously collect, transmit, and analyze health-related data from patients. It provides real-time insights to healthcare professionals, patients, and caregivers to improve patient care, enhance early disease detection, and promote overall well-being. One important component of such a system is the alert system, which notifies relevant parties when certain health parameters or conditions require attention.

Development of a patient health monitoring system based on the internet of things with a module for predicting vital signs

<span>Recent issues related to human health in the world have shown the importance of telemedicine considering necessities to perform the remote monitoring of patients. In this study, using a patient smart monitoring system (PSMS), we collected 5,000 samples of heart rate and blood saturation vital signs from 4 volunteers and tried to find better correlation algorithms to develop a module to predict what these vital signs will be in the next 60 seconds. The following regression algorithms recurrent neural network (long short-term memory) (RNN(LSTM)), autorregresive integrated moving average (ARIMA), value-added reseller vector autoregression (VAR) were used to forecast the patient's state of health in the next 60 seconds. Further, the support vector machine (SVM) and Naive Bayes classification algorithms use the data forecasted by the regression algorithms as input to predict the health status of the patients. When comparing algorithms, we focused on the F measure, a metric used to evaluate the performance of machine learning algorithms. As a result, RNN(LSTM) and SVM showed the performance score value of machine learning algorithms F 0.84, RNN(LSTM) and Naive Bayes 0.81, VAR and SVM 0.82, and VAR and Naive Bayes 0.75. Compared to them, the correlation of ARIMA regression algorithms and SVM classification showed a better F score of 0.86 for machine learning algorithms than the others.</span>

Проблемные вопросы разработки системы удалённого мониторинга здоровья

Summary. The aim of the study is to develop a remote patient health monitoring system. The research methodology includes three main parts: the first part is detecting the fact that the patient has fallen; the second part is taking electrocardiogram — ECG (heartbeat detection); the third part is transmitting the data for its remote viewing by a medical professional. Results of the study and their analysis. The main objective of the study was achieved. All three individual modules — patient fall detection module, heartbeat detection module and remote viewing module — gave the planned results. It is concluded that all the modules of the system can be further optimized and brought to a final unified scheme.

The Future of HealthCare is Connected

An IoT based patient health monitoring system utilising ESP32 is a revolutionary method for enhancing healthcare services by enabling remote and real-time monitoring of patients health parameters.The system uses the ESP32 microcontroller and the Internet of Things (IoT) to effectively send and process data.The system uses numerous wireless sensors and when patient comes in contact with these sensors,It reads and captures critical physiological data like heart rate, blood pressure, body temperature, and oxygen saturation. Data from various sensors is received by the ESP32 microcontroller, which acts as a hub for data gathering, processing, and connection with a cloud-based platform.The cloud platform receives the health data provided by the ESP32 microcontroller.

Retracted: IoT-Based Smart Health Monitoring System for COVID-19 Patients

Retracted: IoT-Based Smart Health Monitoring System for COVID-19 Patients

Sistem Cerdas Pemantau Kesehatan Pasien Lanjut Usia Berbasis IoT (Hardware)

The Internet of Things (IoT) is a technological innovation that is widely used in science, including the field of health sciences. An intelligent health monitoring system for elderly patients is only one of the IoT applications that might be developed in the field of health sciences. This system must be implemented so that doctors can ascertain the patient's status and that patients can be monitored from home rather than having to visit the hospital for checkups. Therefore, a device is required to make it simpler for patients and medical professionals to check the three vital signs—heart rate, oxygen saturation, and body temperature. Vital signs are a variety of physiological statistical measurements that are used to assess a patient's health state, particularly in elderly people (elderly) who are ill or at risk. With the use of this instrument, the patient can provide the doctor with information on his vital signs, making it simpler for the doctor to identify the patient's condition and treat the patient. This tool's design makes use of the MAX30100 sensor, which measures oxygen saturation and heart rate. Then comes the MLX90614 sensor, which measures body temperature. The microcontroller (ESP32) processes the sensor before sending the data to the MySQL database. The percentage accuracy rate for testing the heart rate is 96.15%, the percentage accuracy level for testing the oxygen level is 97.4%, and the percentage accuracy level for testing the body temperature is 98.3%.

Design and Implementation of IOT Based Patient's Health Monitoring System

Design and Implementation of IOT Based Patient's Health Monitoring System

Internet of things and cloud controlled remote health management system in telemedicine for healthcare facilities

Telemedicine, one of the most well-liked and quickly evolving medical applications, enables patients and the general public to access better and quicker medical care. To enable the process of acquiring detailed information indicative of our physical and mental health for the optimum patient health monitoring, networked sensors are either wearables or implanted in our everyday environments. This project provides a detailed explanation of the methodology used as well as a number of design factors that should be taken into account when developing a patient health monitoring system. This method records the patient's vital signs, such as their heart rate and temperature, and sends an alert to the desired smartphone app.The proposed system is primarily intended for the countryside Indian population who suffers from hypertension and hypotension. It is also programmed to detect pre-cardiac arrest situations so that people in remote areas can take precautions and seek help from a medical or healthcare unit. Biomedical and photo-electronic sensors are employed to record the input parameters, with the analytical portion being handled by a computer. Live server demonstrations of the outcomes are made.

IoT Based Covid Patient Health Monitoring System

The COVID-19 pandemic situation has made timely Health monitoring a major concern for saving a life. The busy routine life is a major challenge for doctors to regularly monitor the Health of a COVID Patient. To overcome this problem faced by the family members and doctors in this pandemic situation, we have designed a smart Health monitoring system using IoT (Internet of Things). This system immediately checks and alerts about the critical situations faced by the COVID Patients. This system uses NODEMCU ESP8266 controller, for continuous tracking of the Patient’s Blood Oxygen saturation (SpO 2 ) level. Additionally, Patient Health parameters such as Body Temperature, Heart rate (BPM), as well as Room Temperature and Humidity can also be measured. The Patient Health data are collected and stored in an IoT platform (BLYNK). These Patient Health parameters can be monitored from anywhere by any devices such as Smartphones, Laptop, PC, Smart TV’s etc… that supports browsing capabilities. If the levels of Blood Oxygen Saturation (SpO 2 ) gets lower, it sends a warning immediately to the family members, doctors and to the nearest hospital through mail, phone call and SMS (Short Message Service). By Checking the data obtained from the system, necessary actions could be taken before the problem becomes severe. Thus, the Patient’s life can be saved in time by using this proposed system.

An intelligent health monitoring and diagnosis system based on the internet of things and fuzzy logic for cardiac arrhythmia COVID-19 patients

Background:During the COVID-19 pandemic, there is a global demand for intelligent health surveillance and diagnosis systems for patients with critical conditions, particularly those with severe heart diseases. Sophisticated measurement tools are used in hospitals worldwide to identify serious heart conditions. However, these tools need the face-to-face involvement of healthcare experts to identify cardiac problems. Objective:To design and implement an intelligent health monitoring and diagnosis system for critical cardiac arrhythmia COVID-19 patients. Methodology:We use artificial intelligence tools divided into two parts: (i) IoT-based health monitoring; and (ii) fuzzy logic-based medical diagnosis. The intelligent diagnosis of heart conditions and IoT-based health surveillance by doctors is offered to critical COVID-19 patients or isolated in remote locations. Sensors, cloud storage, as well as a global system for mobile texts and emails for communication with doctors in case of emergency are employed in our proposal. Results:Our implemented system favors remote areas and isolated critical patients. This system utilizes an intelligent algorithm that employs an ECG signal pre-processed by moving through six digital filters. Then, based on the processed results, features are computed and assessed. The intelligent fuzzy system can make an autonomous diagnosis and has enough information to avoid human intervention. The algorithm is trained using ECG data from the MIT-BIH database and achieves high accuracy. In real-time validation, the fuzzy algorithm obtained almost 100% accuracy for all experiments. Conclusion:Our intelligent system can be helpful in many situations, but it is particularly beneficial for isolated COVID-19 patients who have critical heart arrhythmia and must receive intensive care.

Remote Health Monitoring System for Patients

In this paper, we introduce the Virtual Clinic, a hardware and software system that enables patients and doctors to communicate remotely. To utilize the system, patients must have a wearable medical device and a mobile phone, while doctors require access to a computer device. The Virtual Clinic allows for the quick exchange of vital data and messages between patients and doctors, and even employs machine learning techniques to diagnose diseases automatically. This system provides the opportunity to use telemedicine and telehealth concepts to remotely monitor a patient's health, offer personalized health services, manage drug treatments, handle chronic illnesses, provide psychological services, give second opinions, collaborate on treatments, and quickly respond to emergencies. Our Virtual Clinic system, which utilizes IoT, network, and cloud technologies, is a versatile tool suitable for individuals, medical clinics and trainers alike.

Patient health monitoring system using IoT

Healthcare monitoring systems have been one of the most important system in the last decade, and they have become increasingly technological. Humans are facing various issue and untimely death due to multiple illness, due to a lack of medical treatment for patients. To overcome this issue a real time health monitoring system has proposed based on IOT. The proposed system consists of mobile application and GSM for continuous wireless monitoring of patients. The main aim is to create a dependable patient management system based on IoT so that healthcare professionals can monitor their patients who are either hospitalized or at home using an IOT based integrated healthcare system to ensure quality patient care. Sensors are used to track vital parameters, and the data collected by the sensors is sent to the cloud via a WiFi module. A wireless healthcare monitoring system has created that can provide real time online information about a patients conditions. The system is made up of sensors, a data acquisition unit, a microcontroller (i.e. ESP32) and software. The patients temperature, heart beat rate, ECG, blood pressure and SPO2 are regularly monitored, displayed and stored by the system and the same has been sent to the doctors mobile containing the application. Additionally a message is sent to the doctors mobile if any of the parameter crosses the threshold value. Thus, IOT based real time health monitoring system systematically monitor the condition of patients health and save their life on time.

Two-Way Approach for Improved Real-Time Transmission in Fog-IoT-Based Health Monitoring System for Critical Patients

Health monitoring systems are now required, particularly for essential patients, following the COVID-19 pandemic, which was followed by its variants and other epidemics of a similar nature. Effective procedures and strategies are required, though, to react promptly to the enormous volume of real-time data offered by monitoring equipment. Although fog-based designs for IoT health systems typically result in enhanced services, they also give rise to issues that need to be resolved. In this paper, we propose a two-way strategy to reduce network latency and use while increasing real-time data transmission of device gateways used for sensors by making educated judgments for connection setup with BS and task assignment. For this, a simulation using iFogSim in the Eclipse IDE showed how effective the suggested strategy for massive IoT health monitoring systems is. The algorithm is analyzed for network usage and latency, and the results reveal 20%–25% improvements compared to the existing methods regarding network usage and latency.

Patient Health Monitoring System Development using ESP8266 and Arduino with IoT Platform

Patient Health Monitoring System Development using ESP8266 and Arduino with IoT Platform

Remote Health Care Monitoring System using IoT for Asthma/Heart Patients

Abstract: Healthcare has now become a big concern in almost everyone's socioeconomic routine. With the digitalization of lifestyle, the era of modern human evaluation began, making people robust and machine dependent. This leads to the numerous amounts of data that are scattered everywhere in each and every move in his day-to-day life. Digitization transformed basic information technology into intelligent technology by altering human lifestyle. Additionally, the world's population is expanding, which means the occurrence of numerous chronic heart illnesses. Consequently, more people get sick, hospitals are congested, and the death rate rises. Additionally, this technology transmits real-time data on patients' heartbeat and temperature with records over the Internet. Therefore, a patient health monitoring system based on IoT uses the internet to efficiently monitor patient health. It also allows users to monitor their loved ones while they are at work and has even saved lives. Also, NodeMCU is used as the web server to track patient health while using this monitoring system. Consequently, patient health indicators like body temperature, heart rate, and blood oxygen level are get monitored and stores the data. Also, Big data is a concept that can be applied to better health monitoring.