IoT-Based Remote Health Monitoring System Employing Smart Sensors for Asthma Patients during COVID-19 Pandemic

COVID-19 and asthma are respiratory diseases that can be life-threatening in uncontrolled circumstances and require continuous monitoring. A poverty-stricken South Asian country like Bangladesh has been bearing the brunt of the COVID-19 pandemic since its beginning. The majority of the country’s population resides in rural areas, where proper healthcare is difficult to access. This emphasizes the necessity of telemedicine, implementing the concept of the Internet of Things (IoT), which is still under development in Bangladesh. This paper demonstrates how the current challenges in the healthcare system are resolvable through the design of a remote health and environment monitoring system, specifically for asthma patients who are at an increased risk of COVID-19. Since on-time treatment is essential, this system will allow doctors and medical staff to receive patient information in real time and deliver their services immediately to the patient regardless of their location. The proposed system consists of various sensors collecting heart rate, body temperature, ambient temperature, humidity, and air quality data and processing them through the Arduino Microcontroller. It is integrated with a mobile application. All this data is sent to the mobile application via a Bluetooth module and updated every few seconds so that the medical staff can instantly track patients’ conditions and emergencies. The developed prototype is portable and easily usable by anyone. The system has been applied to five people of different ages and medical histories over a particular period. Upon analyzing all their data, it became clear which participants were particularly vulnerable to health deterioration and needed constant observation. Through this research, awareness about asthmatic symptoms will improve and help prevent their severity through effective treatment anytime, anywhere.

IoT-Based Health Monitoring System Development and Analysis

This paper presents the design and implementation of a health monitoring system using the Internet of Things (IoT). In present days, with the expansion of innovations, specialists are always looking for innovative electronic devices for easier identification of irregularities within the body. IoT-enabled technologies enable the possibility of developing novel and noninvasive clinical support systems. This paper presents a health care monitoring system. In particular, COVID-19 patients, high blood pressure patients, diabetic patients, etc., in a rural area in a developing country, such as Bangladesh, do not have instant access to health or emergency clinics for testing. Buying individual instruments or continuous visitation to hospitals is also expensive for the regular population. The system we developed will measure a patient’s body temperature, heartbeat, and oxygen saturation (SpO2) levels in the blood and send the data to a mobile application using Bluetooth. The mobile application was created via the Massachusetts Institute of Technology (MIT) inventor app and will receive the data from the device over Bluetooth. The physical, logical, and application layers are the three layers that make up the system. The logical layer processes the data collected by the sensors in the physical layer. Media access management and intersensor communications are handled by the logical layer. Depending on the logical layer’s processed data, the application layer makes decisions. The main objective is to increase affordability for regular people. Besides sustainability in the context of finance, patients will have easy access to personal healthcare. This paper presents an IoT-based system that will simplify the utilization of an otherwise complicated medical device at a minimum cost while sitting at home. A 95 percent confidence interval with a 5 percent maximum relative error is applied to all measurements related to determining the patient’s health parameters. The use of these devices as support tools by the general public in a certain situation could have a big impact on their own lives.

Internet of Things- (IoT-) Based Real-Time Vital Physiological Parameter Monitoring System for Remote Asthma Patients

In this paper, the design and implementation of an asthma patient monitoring system are presented. It can be categorized as an IoT-based device which can monitor heart rate, room temperature, humidity, air quality, nostril temperature, and oxygen saturation (SpO2) using an ESP8266 microcontroller and different sensors. All data will be sent to Firebase via Wi-Fi and then to patients and doctors via mobile applications and websites, respectively. Doctors can access this data via a website and instruct patients based on their specific needs. An ESP8266 microcontroller, a DHT11 temperature and humidity sensor, a MAX30100 pulse and SpO2 sensor, a MQ-135 air quality sensor, and an LM-35 temperature sensor have been used for the proposed system in this research. This system uses an efficient way to store all the data and information about the patient. It also carries their various test reports. The data is preserved in a proper way, which will help the patient or doctor retrieve any information in a smooth and swift manner. The software part of the system is divided into two parts. One of them is a mobile application that was designed by the Android Studio. The second one is the website, and it is also divided into two parts. HTML (hypertext markup language), CSS (cascading style sheets), and JavaScripts are designed for the front end of the website, and PHP (hypertext preprocessor) is used for the backend of the website. The device is tested on seven different real human test subjects, and the accuracy of that device is state of the art. It can measure the heart rate of a person along with their oxygen saturation with accuracy. Also, this device calculates the humidity properly as it changes the moment wet towels are put near the sensors and far from the sensors. It also shows the proper air quality as it has been tested with smoke to measure the extreme difference in the air quality. This system also has video calling and prescription features. Using this system, patients can consult with the doctor and get a prescription. Using this system, the vital physiological parameters of the remote patients can be measured and monitored. Any hospital or diagnostic center can be more efficient and organized by using a system. And patients are also freed from the hustle as they do not need to go and visit the doctor over and over again. The system is designed in such a way that it can look after the particular needs of a typical hospital or a diagnostic center and is able to provide easy and efficient storage of information related to patients.

Development of NLP-Integrated Intelligent Web System for E-Mental Health.

As the COVID-19 pandemic continues, the need for a better health care facility is highlighted more than ever. Besides physical health, mental health conditions have become a significant concern. Unfortunately, there are few opportunities for people to receive mental health care. There are inadequate facilities for seeking mental health support even in big cities, let alone remote areas. This paper presents the structure and implementation procedures for a mental health support system combining technology and professionals. The system is a web platform where mental health seekers can register and use functionalities like NLP-based chatbot for personality assessment, chatting with like-minded people, and one-to-one video conferencing with a mental health professional. The video calling feature of the system has emotion detection capabilities using computer vision. The system also includes downloadable prescription facilities and a payment gateway for secure transactions. From a technological aspect, the conversational NLP-based chatbot and computer vision-powered video calling are the system's most important features. The system has a documentation facility to analyze the mental health condition over time. The web platform is built using React.js for the frontend and Express.js for the backend. MongoDB is used as the database of the platform. The NLP chatbot is built on a three-layered deep neural network model that is programmed in the Python language and uses the NLTK, TensorFlow, and Keras sequential API. Video conference is one of the most important features of the platform. To create the video calling feature, Express.js, Socket.io, and Socket.io-client have been used. The emotion detection feature is implemented on video conferences using computer vision, Haar Cascade, and TensorFlow. All the implemented features are tested and work fine. The targeted users for the platform are teenagers, youth, and the middle-aged population. Mental health-seeking is still considered taboo in some societies today. Apart from basic established facilities, this social dilemma of undergoing treatment for mental health is causing severe damage to individuals. A solution to this problem can be a remote platform for mental health support. With this goal in mind, this system is designed to provide mental health support to people remotely from anywhere worldwide.

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data.

The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people's feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people's minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public's opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.

Consumer side economic perception of telemedicine during COVID-19 era: A survey on Bangladesh's perspective.

In Bangladesh, the telemedicine industry is one of the few industries able to flourish in the contemporary era of COVID-19. But to thrive, the industry must know the viewpoints of both consumers (those who are interested in availing the services of the industry) and non-consumers to overcome deficits. This should be done to maximize profits and give optimal utility to users so that the industry can be made sustainable in the long run. The main aim of this paper is to analyze the economic perception of both the telemedicine consumers and non-consumers of Bangladesh and the actions required to be taken to optimize them. A survey was developed with 18 questions divided into several parts relating to the health identity of the respondent, the respondents' use of telemedicine, the analysis of the economic behaviors of the respondents with regards to telemedicine, and the consumer perception of the merits and demerits of telemedicine. The survey results show that about one-third has used some form of telemedicine during the COVID-19 pandemic. Among the telemedicine users, 48% used hospital-mandated telemedicine services whereas 41% used mobile telemedicine applications. The survey states that 75% were satisfied with the service they received. The average payment made by the respondent population was 532 Taka, and 62% of them thought that the amount they paid was justified. In conclusion, the results of this survey can be utilized in making economically viable telemedicine models that will give optimal utility to its consumers and help forecast the next stage of the industry for betterment in the health sector.

IoT-Based Smart Health Monitoring System for COVID-19 Patients.

During the ongoing COVID-19 pandemic, Internet of Things- (IoT-) based health monitoring systems are potentially immensely beneficial for COVID-19 patients. This study presents an IoT-based system that is a real-time health monitoring system utilizing the measured values of body temperature, pulse rate, and oxygen saturation of the patients, which are the most important measurements required for critical care. This system has a liquid crystal display (LCD) that shows the measured temperature, pulse rate, and oxygen saturation level and can be easily synchronized with a mobile application for instant access. The proposed IoT-based method uses an Arduino Uno-based system, and it was tested and verified for five human test subjects. The results obtained from the system were promising: the data acquired from the system are stored very quickly. The results obtained from the system were found to be accurate when compared to other commercially available devices. IoT-based tools may potentially be valuable during the COVID-19 pandemic for saving people's lives.