ABSTRACT
With the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. The distance measurement accuracy heavily affects the probability estimation of being infected. Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier based on Convolutional Deep Neural Network is provided. In particular, the raw data coming from the accelerometer sensor of a smartphone are arranged to form an image including several channels (HAR-Image), which is used as fingerprints of the in-progress activity that can be used as an additional input by tracking applications. Experimental results, obtained by analyzing real data, have shown that the HAR-Images are effective features for human activity recognition. Indeed, the results on the k-fold cross-validation and obtained by using a real dataset achieved an accuracy very close to 100%.
ABSTRACT
This work focuses on the development of a portable physiological monitoring framework that can continuously monitor the patient's heartbeat, oxygen levels, temperature, ECG measurement, blood pressure, and other fundamental patient's data. As a result of this, the workload and the chances of being infected by COVID-19 of the health workers will be reduced and an efficient patient monitoring system can be maintained. In this paper, an IoT based continuous monitoring system has been developed to monitor all COVID-19 patient conditions and store patient data in the cloud server using Wi-Fi Module-based remote communication. In this monitoring system, data stored on IoT platform can be accessed by an authorized individual and ailments can be examined by the doctors from a distance based on the values obtained. If a patient's physical condition deteriorates, the doctor will immediately receive the emergency alert notification. This model proposed in this research work would be extremely important in dealing with the Corona epidemic around the world. © 2022 IEEE.
ABSTRACT
This paper describes how an IoT -based health monitoring system was conceived and built (IoT). With the proliferation of new technologies, doctors nowadays are constantly on the lookout for cutting-edge electronic tools that will make it simpler to detect abnormalities in the human body. The Internet of Things makes it possible to create cutting-edge, non-intrusive healthcare assistance systems. In this article, we introduce the Comprehensive Health Monitoring System, or CHMS. Normal people can't afford to buy separate devices or make frequent trips to hospitals. Our CHMS will monitor a patient's vitals, including temperature, heart rate, and oxygen saturation (OS), and relay that information to a portable device. To make sense of the information gathered by the physical layer's sensors, the logical layer must analyses it. The application layer then makes judgments based on the processed data from the logical layer. The primary goal is to reduce costs for average consumers. Patients will have simple access to individual healthcare, in addition to financial sustainability. This study introduces an IoT -based system that would streamline the operation of a complex medical gadget while reducing its associated cost, allowing its users to do so from the comfort of home. The public's adoption of these gadgets as aids in a given setting might have significant effects on their own lives. © 2023 IEEE.
ABSTRACT
The entire world witnessed the covid-19pandemicinthe year 2020. The actual outbreak of this corona virus was first reported in Wuhan, China and later declared to be epidemic by (WHO) World Health Organization. The whole world was under tremendous pressure in monitoring health, managing, and maintaining hospitals and inventing new drugs. Initially, India was very much worried because of the huge population. The pandemic posed a critical challenge for healthcare sectors, since doctors and nursing professionals were among the most severely affected and it's clear that India must adopt new measures to increase healthcare proportional ratio and adoption of new technologies to manage large population groups. Robotics is one area which may largely always support the segment. The proposed research project emphasized on developing robotic devices with robotic vision, sensors-based motion planning, dynamic obstacle detection, and autonomous navigation in a hospital environment and supported the medical and nursing teams in reducing their workload and improving patient health monitoring, also the research explored multi-robot exploration and integration. © 2022 IEEE.
ABSTRACT
This paper presents an IoT-based heart monitoring system using 8266 NodeMCU. According to the Malaysian Department of Statistics, ischemic heart disease is the leading cause of death, accounting for 15.0% of the 109,164 medically certified deaths in 2019. The coronary heart is a vital organ that pumps oxygen and blood across the body. Meanwhile, if the heart is not getting sufficient oxygen, the patient will experience chest pain, typically on the left side of the body, which can be mistaken for a heart problem. During the Covid-19 pandemic, a patient cannot attend regular treatment at the hospital as it is operating at full capacity. During this phase, the hospital can only focus on the critical and high-risk patient. The proposed heart monitoring system monitors the patient by measuring the heart rate and oxygen level in the comforts of home. Therefore, the patient can provide his current health record for the doctor's evaluation. The idea behind this proposed system is to construct an IOT-based system that automatically monitors the health condition in terms of heartbeat and oxygen detection. The prototype provides data to the Blynk for the patient and the I-Heart web-based application for the medical practitioner. © 2022 IEEE.
ABSTRACT
This paper discusses the design and implementation of an Internet of Things (IoT)-based telemedicine health monitoring system (THMS) with an early warning scoring (EWS) function that reads, assesses, and logs physiological parameters of a patient such as body temperature, oxygen saturation level, systemic arterial pressure, breathing patterns, pulse (heart) rate, supplemental oxygen dependency, consciousness, and pain level using Particle Photon microcontrollers interfaced with biosensors and switches. The Mandami fuzzy inference-based medical decision support system (FI-MDSS) was also developed using MATLAB to assist medical professionals in evaluating a patient's health risk and deciding on the appropriate clinical intervention. The patient's physiological measurements, EWS, and health risk category are stored on the Particle cloud and Thing Speak cloud platforms and can be accessed remotely and in real-time via the Internet. Furthermore, a RESTful application programming interface (API) was developed using GO language and PostgreSQL database to enhance data presentation and accessibility. Based on the paired samples t-tests obtained from 6 sessions with 10 trials for each vital sign per session, there were no significant differences between the clinical data obtained from the designed prototype and the commercially sold medical equipment. The mean differences between the compared samples for each physiological data were not more than 0.40, the standard deviations were less than 2.3, and the p-values were greater than 0.05. With a 96.67% accuracy, the FI-MDSS predicted health risk levels that were comparable to conventional EWS techniques such as the Modified National Early Warning Score (m-NEWS) and NEWS2, which are used in the clinical decision-making process for managing patients with COVID-19 and other infectious illnesses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
ZigBee technology is preferably been used for health monitoring as it consumes very less power, high reliability, and low expenses. In this paper, mobile-based medical alert system for COVID-19 detection system using ZigBee technology is proposed. The health report of the user will be sent to the caretaker or doctor via cloud computing network so that they can analyze the problem. The real-time monitoring of health temperature and symptoms of COVID-19 and data transmission via remote sensing is also realized. © 2022 Scrivener Publishing LLC.
ABSTRACT
The IoT (Internet of Things), a network of interconnected systems and data analytics, which can provides information about the spread of diseases/Virus globally. Typically, IoT is a bridge between machine learning philosophy, real time application such as security system, smart lights, smart speakers and many more. [1.2]. In current situation (pandemic), all over the world, is facing the problem where all are sucked down and looking for solution which can resolve the problem with cost -effective solution that has risen. Researchers are looking forward for the challenges and describing the studies which can overcome with the by IoT. The brief review aimed to significant applications over the COVID-19. © 2022 IEEE.
ABSTRACT
Amid and post-COVID-19 pandemic, the matter of being in touch with patients to monitor their health matrices became somewhat challenging, especially in the rural areas of countries like Bangladesh and for elderlies. To address this issue, a patient health monitoring system is developed using a Programmable Intelligent Computer (PIC) microcontroller and Global System for Mobile Communications (GSM) protocol with the help of a pulse sensor, IR sensor, photodiodes, temperature sensor, etc., to measure 3 (three) crucial health matrices such as heartbeat/pulse, oxygen saturation level, and body temperature from a fingertip of the patient in 20 s remotely. Whenever the system measures the health matrices, it sends a short message service (SMS) report to a personal caretaker over GSM automatically. If the system finds any anomaly based on predefined threshold levels for each health parameter, it sends a SMS alert report to the designated doctor automatically as well. A prototype of the developed system is made, verified, and tested to be working perfectly as designed and programmed. In the experiment with the developed system, heart rate ranged from 61 to 105 bmp, body temperature ranged from 95.3 to 99.1 ℉, and oxygen saturation was minimum at 97%. According to the set threshold levels, which led to an automatic SMS alert to the caretaker's mobile phone. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Medical specialists are primarily interested in researching health care as a potential replacement for conventional healthcare methods nowadays. COVID-19 creates chaos in society regardless of the modern technological evaluation involved in this sector. Due to inadequate medical care and timely, accurate prognoses, many unexpected fatalities occur. As medical applications have expanded in their reaches along with their technical revolution, therefore patient monitoring systems are getting more popular among the medical actors. The Internet of Things (IoT) has met the requirements for the solution to deliver such a vast service globally at any time and in any location. The suggested model shows a wearable sensor node that the patients will wear. Monitoring client metrics like blood pressure, heart rate, temperature, etc., is the responsibility of the sensor nodes, which send the data to the cloud via an intermediary node. The sensor-acquired data are stored in the cloud storage for detailed analysis. Further, the stored data will be normalized and processed across various predictive models. Among the different cloud-based predictive models now being used, the model having the highest accuracy will be treated as the resultant model. This resultant model will be further used for the data dissemination mechanism by which the concerned medical actors will be provided an alert message for a proper medication in a desirable manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Work-from-home policies have been the standard since the worldwide pandemic breakout, and this has spurred the fast development of applications in the area of IoT for remotely monitoring and managing applications. This has encouraged us to design and develop a remotely controlled robotic arm that can be used in applications where the engaging human hazardous environment (such as quarantined rooms of COVID affected patients) is dangerous. This has led to the development of a B-rover called a robotic arm, which the technicians remotely control to reduce the direct contact between the technician and the hazardous environment. It has various applications, such as a health monitoring system for monitoring the patient's health conditions, sample collection from the patients and the capability of the Robotic Arm to deliver medications to the COVID affected patients without engaging humans. It is proposed to design a 3DOF(degrees of freedom) robotic arm with stepper motor which is controlled through Wi-Fi using the BlynkIoT App with widgets like Joystick and Sliders. This will pick and drop the objects from one place to another. The results show that the designed robotic arm shows a 3% variation from the simulated and actual results when the slider is adjusted. © 2022
ABSTRACT
With the present Coronavirus disease (COVID-19) pandemic, Internet of Things (IoT)-based health monitoring devices are precious to COVID-19 patients. We present a real-time IoT-based health monitoring system that monitors patients' heart rate and oxygen saturation, the most significant measures necessary for critical care. Specifically, the proposed IoT-based system is built with Arduino Uno-based hardware and a web application for retrieving the patients' health information. In addition, we implement the Autoregressive Integrated Moving Average (ARIMA) method in the back-end server to predict future patient measurements based on current and past measurements. Compared to commercially available devices, the system's results are adequately accurate, with an acceptable RMSE for predicted value. © 2022 IEEE.
ABSTRACT
Healthcare sectors are majorly moving towards Remote Health Monitoring Systems (RHMS) after the COVID-19 pandemic outbreak across the world. RHMS involves monitoring the patient's vital parameters remotely and providing advice and consultation online. Alerts are generated whenever a particular health parameter exceeds the threshold and sent to the medical officers for further actions. However, it is observed that these thresholds are applicable only when a patient is at rest and can change drastically during patient's physical activity such as walking, climbing the staircase, during exercise etc., which can mislead in understanding the patient's health condition. Hence there is a requirement to correlate these parameter values with the current activity the patient is in and to generate activity-based dynamic thresholds. In this paper, a method to correlate the sensor values with physical activities is proposed. The activity-based RHMS (aRHMS) uses the motion sensors available in the patient's smartphone to predict the activity and will automatically adjust the threshold values in co-relation with the activities and provides alarms/alerts accordingly. © 2022 IEEE.
ABSTRACT
Technological advancements in medical field gave birth to smart watches, handheld devices that can read your vital signs real-time. However, home quarantined COVID-19 patients, even with the help of smartwatches, are still needed to be monitored physically by health practitioners, therefore posing a threat of transmission of the virus. This paved the way to the investigation of designing a wearable device that read health vital statistics and the location of home quarantined patients and a system that will remotely monitor it. Thus, this work developed Vitaband, a health-monitoring system made up of a node, gateway, and a web application. The node consists of sensors - pulse meter, oximeter, IR sensor and GPS module - that will read the vital signs of the patient and display it through an OLED screen. Two Raspberry Pico Pi microcontrollers will process the data gathered by these sensors and send them to the gateway through the Lora module. The gateway then, housing the ESP32 microcontroller, will connect to the internet and transmit the received data to the MongoDB database. The web application finally, which is programmed using REACT framework, shall display the data for remote monitoring. Vitaband is tested and evaluated using the ISO/IEC 25010 model. Results revealed that Vitaband received an overall rating of Very Satisfactory from the Bulacan State University Nursing student during their training as the respondents and Excellent from medical professionals which are registered nurses and barangay health workers, respectively. © 2022 IEEE.
ABSTRACT
WHO reports that there is an increase in heart, neurological, and cancer diseases. There is also the onset of global emergencies like the Novel Coronavirus. WHO declared COVID-19 a "global emergency" due to its rapid spread. The virus caused economies to crash. During the last few years despite the scarcity of public data, researchers made progress to estimate the pandemic's intensity, Progression, and transmission modes. Medical experts opine that there is a paucity of doctors and paramedics. To bridge this some functions of doctors need automation. Physicians with sensor data enact not only faster diagnosis but also they are automated. A study on IOT based robot is realized as an intelligent Health monitoring system that monitors patients' parameters. The Haar Cascade algorithm is realized using OpenCV to identify patients and visualize their parameters. Predictive algorithms like Random forest and linear regression were realized. Depending on the disease, an automatic dispenser dispenses medicine on timelines. The accuracy of prediction and detection is 98%. Copyright © 2022, Anka Publishers. All rights reserved.
ABSTRACT
The COVID-19 outbreak presents a major challenge in diagnosing and monitoring respiratory diseases. IoT has the potential to address the challenges by remotely providing patients with rich information about respiratory health. However, current IoT-based health monitoring systems do not provide users with sufficient information to access the rich information in Health Social Network (HSN). We developed PhysioVec, a framework for searching HSN using breath sounds. PhysioVec consists of three components: Local Recurrent Transformer (LRT), a Multivariate radial-basis Logistic Interpreter (MLI), and an existing sentence embedding module. LRT combines local attention and recurrent Transformer to reduce overfitting and improve performance in the segmentation of breathing sounds. Physiological information detected from breathing sounds is used to search for relevant health information. PhysioVec achieved 100%., 59.8%., 92.2%., and 100% precision in the top one search results for breath sound with the common cold, influenza, pneumonia, and bronchitis, respectively. Our proposed framework allows users to search HSN for useful information just by recording their breathing sounds on mobile phones. © 2022 IEEE.
ABSTRACT
Monitoring Covid-19 patients is extremely challenging due to under-resourced or risk of infection. With the increased demand for hospital beds and the difficulty of delivering care, some health centers have advised individual with milder symptoms to stay home. Hence, this paper presents a health monitoring system based on IoT that helps the medical staff to monitor blood saturation, heart rate, pulse rate and body temperature remotely. A Biosensor Module MAX3100 is used to read blood saturation level and heart rate of the patient while body temperature sensor, DS18B20 is employed to scan the body temperature. The measurement of room temperature and humidity level is done through humidity sensor. ESP32 Arduino will encode and decode all input data before execution process. The patient's fingers are connected to the sensors and the data is displayed on the smart phone or PC. The proposed system was tested and provide the intended output. Therefore, with the aid of this proposed system, medical staff can examine and keep track on several patients' status simultaneously and without the hassle of being infected by the virus as it is monitored remotely. © 2022 IEEE.
ABSTRACT
As a result of the Covid-19 outbreak, a trustworthy health care system for remote surveillance was required, particularly in care facilitieas for the elderly. Many studies have been done in this subject, however they still have security, latency, extended time of execution and response delay. An intelligent Healthcare infrastructure termed Remote Health Monitoring (RHM) is introduced in this study to overcome these constraints. The framework uses high-level fog layer services including locally storage, native real-time data processing with combined mining of information in handling certain cloud and sensor network loads and transformed in a decision taker entity. This systems uses a body and camera sensors to diagnose, increasing accuracy and efficiency while protecting privacy. The suggested framework was tested using the iFogSim toolbox. It may minimise latency, energy usage, network connectivity and total reaction time. This work will assist develop a high performing, secure, and dependable intelligent Medical infrastructure. © 2022 IEEE.
ABSTRACT
In today's world of medical science, remote patient monitoring devices are becoming more important and a future need particularly in the present COVID-19 situation as individuals are preferred to be kept isolated. Patients would be benefited from a suitable monitoring system that measures their important medical parameters such as pulse rate, oxygen saturation or SpO2, body temperature, blood pressure, and Galvanized Skin Response (GSR). This system can increase the medical staff efficiency by drastically decreasing their duties in hospitals and the need to attend to them individually. Patients in their home isolation may utilize the device as well, and their vital indicators may be checked by doctors remotely. In this work, we are prototyping a powerefficient, wearable medical kit and a resource-aware fog network set up to handle the Internet of Things (IoT) data traffic. The idea behind the design is to process the critical medical sensors' data in the fog nodes which are deployed at the edge of the network. The data thus received, is used for a machine learning-based solution for personal health anomalies and COVID-19 infection risk analysis. © 2022 IEEE.
ABSTRACT
In the Beginning of 2020, most of the countries were affected by COVID-19, especially India. Our government announced lockdown, this situation became worse to many people mainly pregnancy women who needed monthly health check-ups. Our solution for this problem is using IOT Based Pregnancy Woman Health Monitoring System which helps woman in monitoring the health and getting reports just sitting in home. The selected study issue presents an effective Monitoring System for increasing the confidentially of pregnancy women health or otherwise, since it is an essential emotional and psychological event a crucial component in the life of a married lady. This paper gives the survey about the difference we bring from various health monitoring systems developed in recent years. © 2022 IEEE.