Real-Time Health Monitoring System Using Predictive Analytics

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.

An IoT-enabled Electronic Textile-based Flexible Body Sensor Network for Real-time Health Monitoring in Assisted Living during Pandemic

This paper presents a cost-effective and flexible electronic textile sensor with high sensitivity and fast response and demonstrates its versatile applications, including real-time measurements of finger kinematics, phonation, cough patterns, as well as subtle muscle movements (i.e., eye reflex). The sensor can discriminate between speech and cough patterns, thereby expanding its applications to COVID-19 detection, speech rehabilitation training, and human/machine interactions. A combination of different sensor data is essential to acquire clinically significant information. Therefore, a sensor array is interfaced with the LoRa communication protocol to establish an Internet of Things (IoT)-based electronic textile framework. The IoT integration allows remote monitoring of body kinematics and physiological parameters. Therefore, the proposed IoT-based framework holds the potential to provide real-time and continuous health monitoring to allow immediate intervention during this pandemic. © 2022 IEEE.

Real Time Health Monitoring and Oxygen Distribution System for COVID-19 patients using IoT

In developing countries such as India, efficient use of resources and infrastructure is crucial in the light of healthcare crises such as the COVID-19 pandemic. Owing to overcrowded hospitals and inadequate medical infrastructure, traditional ways of examining and monitoring patients are ineffective. For the treatment of Chronic obstructive pulmonary diseases (COPDs) like COVID-19, monitoring a patient's SpO2 level along with the pulse rate is vital. This paper focuses on using IoT devices for documenting essential patient characteristics and performing data analytics on them for future predictions. Pulse oximeter sensor is used to obtain the patient's SpO2 level and pulse rate measurements. This sensor output is processed by Wi-Fi SoC NodeMCU. By unique identification of each patient, this data is displayed via a Mobile application to healthcare workers nearby. By analysing a patient's symptoms, a doctor can remotely regulate the supply of oxygen to the patient with the same mobile application. Machine learning algorithm is trained to analyse and predict a patient's future health conditions. With the adoption of such systems, the existing medical structure could improve vastly in its efficiency and capabilities during a healthcare crisis such as COVID-19. © 2021 IEEE.

Real-Time Patient Ailment Monitoring Framework Collaborating Enhanced CNN Architectures

As the deadly COVID-19 outbreak spreads across the globe, the utilization of IoT in the surveillance of patients can prevent us from facing catastrophic repercussions. This paper aims to develop a real-time health monitoring system where sensors are used to continuously observe the patient's body temperature, heart rate, and oxygen level. A comparison of two CNN architectures, VGG19 and DenseNet was also undertaken for audio signal processing, with VGG19 offering more promising accuracy in identifying coughing. Additionally, as severe coughing can be an alarm for lung diseases, the system identifies the number of consecutive coughing of a patient, as well as the timestamp for it. Moreover, if a patient feels infirm, they can seek assistance from a nearby doctor or nurse through Google's Speech-to-Text API. The data is then transmitted to a centralized database, where clinicians can monitor patients' symptoms in real-time by extracting the data via a web application. © 2021 IEEE.