Even with the most cutting-edge tools, treating and monitoring patients—including children, elders, and suspected COVID-19 patients—remains a challenging activity. This study aimed to track multiple COVID-19-related vital indicators using a wearable monitoring device with an Internet of Things (IOT) focus. Additionally, the technology automatically alerts the appropriate medical authorities about any breaches of confinement for potentially contagious patients by tracking patients’ real-time GPS data. The wearable sensor is connected to a network edge in the Internet of Things cloud, where data are processed and analyzed to ascertain the state of body function. The proposed system is built with three tiers of functionalities: a cloud layer using an Application Peripheral Interface (API) for mobile devices, a layer of wearable IOT sensors, and a layer of Android web for mobile devices. Each layer performs a certain purpose. Data from the IoT perception layer are initially collected in order to identify the ailments. The following layer is used to store the information in the cloud database for preventative actions, notifications, and quick reactions. The Android mobile application layer notifies and alerts the families of the potentially impacted patients. In order to recognize human activities, this work suggests a novel integrated deep neural network model called CNN-UUGRU which mixes convolutional and updated gated recurrent subunits. The efficiency of this model, which was successfully evaluated on the Kaggle dataset, is significantly higher than that of other cutting-edge deep neural models and it surpassed existing products in local and public datasets, achieving accuracy of 97.7%, precision of 96.8%, and an F-measure of 97.75%.

Background: Increase in the internet data has increased the priority in the data extraction accuracy. Accuracy here lies with what data the user has requested for and what has been retrieved. The same large data sets that need to be analyzed make the required information retrieval a challenging task. Objective: To propose a new algorithm in an improved way than the traditional methods to classify the category or group to which each training sentence belongs. Method: Identifying the category to which the input sentence belongs is achieved by analyzing the Noun and Verb of each training sentence. NLP is applied to each training sentence and the group or category classification is achieved using the proposed GENI algorithm so that the classifier is trained efficiently to extract the user requested information. Results: The input sentences are transformed into a data table by applying GENI algorithm for group categorization. Plotting the graph in R tool, the accuracy of the group extracted by the Classifier involving GENI approach is higher than that of Naive Bayes & Decision Trees. Conclusion: It remains a challenging task to extract the user-requested data, when the user query is complex. Existing techniques are based more on the fixed attributes, and when we move with respect to the fixed attributes, it becomes too complex or impossible for us to determine the common group from the base sentence. Existing techniques are more suitable to a smaller dataset, whereas the proposed GENI algorithm does not hold any restrictions for the Group categorization of larger data sets.