An IoT-fuzzy intelligent approach for holistic management of COVID-19 patients.

In this study, an internet of things (IoT)-enabled fuzzy intelligent system is introduced for the remote monitoring, diagnosis, and prescription of treatment for patients with COVID-19. The main objective of the present study is to develop an integrated tool that combines IoT and fuzzy logic to provide timely healthcare and diagnosis within a smart framework. This system tracks patients' health by utilizing an Arduino microcontroller, a small and affordable computer that reads data from various sensors, to gather data. Once collected, the data are processed, analyzed, and transmitted to a web page for remote access via an IoT-compatible Wi-Fi module. In cases of emergencies, such as abnormal blood pressure, cardiac issues, glucose levels, or temperature, immediate action can be taken to monitor the health of critical COVID-19 patients in isolation. The system employs fuzzy logic to recommend medical treatments for patients. Sudden changes in these medical conditions are remotely reported through a web page to healthcare providers, relatives, or friends. This intelligent system assists healthcare professionals in making informed decisions based on the patient's condition.

Efficient Approach for Extracting High-Level B-Spline Features from LIDAR Data for Light-Weight Mapping.

Light-weight and accurate mapping is made possible by high-level feature extraction from sensor readings. In this paper, the high-level B-spline features from a 2D LIDAR are extracted with a faster method as a solution to the mapping problem, making it possible for the robot to interact with its environment while navigating. The computation time of feature extraction is very crucial when mobile robots perform real-time tasks. In addition to the existing assessment measures of B-spline feature extraction methods, the paper also includes a new benchmark time metric for evaluating how well the extracted features perform. For point-to-point association, the most reliable vertex control points of the spline features generated from the hints of low-level point feature FALKO were chosen. The standard three indoor and one outdoor data sets were used for the experiment. The experimental results based on benchmark performance metrics, specifically computation time, show that the presented approach achieves better results than the state-of-the-art methods for extracting B-spline features. The classification of the methods implemented in the B-spline features detection and the algorithms are also presented in the paper.

Real-time artificial intelligence based health monitoring, diagnosing and environmental control system for COVID-19 patients.

By upgrading medical facilities with internet of things (IoT), early researchers have produced positive results. Isolated COVID-19 patients in remote areas, where patients are not able to approach a doctor for the detection of routine parameters, are now getting feasible. The doctors and families will be able to track the patient's health outside of the hospital utilizing sensors, cloud storage, data transmission, and IoT mobile applications. The main purpose of the proposed research-based project is to develop a remote health surveillance system utilizing local sensors. The proposed system also provides GSM messages, live location, and send email to the doctor during emergency conditions. Based on artificial intelligence (AI), a feedback action is taken in case of the absence of a doctor, where an automatic injection system injects the dose into the patient's body during an emergency. The significant parameters catering to our project are limited to ECG monitoring, SpO2 level detection, body temperature, and pulse rate measurement. Some parameters will be remotely shown to the doctor via the Blynk application in case of any abrupt change in the parameters. If the doctor is not available, the IoT system will send the location to the emergency team and relatives. In severe conditions, an AI-based system will analyze the parameters and injects the dose.

Process techniques for human thoracic electrical bio-impedance signal in remote healthcare systems.

Analysis of thoracic electrical bio-impedance (TEB) facilitates heart stroke volume in sudden cardiac arrest. This Letter proposes several efficient and computationally simplified adaptive algorithms to display high-resolution TEB component. In a clinical environment, TEB signal encounters with various physiological and non-physiological phenomenon, which masks the tiny features that are important in identifying the intensity of the stroke. Moreover, computational complexity is an important parameter in a modern wearable healthcare monitoring tool. Hence, in this Letter, the authors propose a new signal conditioning technique for TEB enhancement in remote healthcare systems. For this, the authors have chosen higher order adaptive filter as a basic element in the process of TEB. To improve filtering capability, convergence speed, to reduce computational complexity of the signal conditioning technique, the authors apply data normalisation and clipping the data regressor. The proposed implementations are tested on real TEB signals. Finally, simulation results confirm that proposed regressor clipped normalised higher order filter is suitable for a practical healthcare system.