The health monitoring system is one of the most innovative technologies that has gained traction in the Internet of Medical Things (IoMT). It allows the connection of multiple sensors and actuators that can capture and monitor the data through the web page or mobile application. IoMT technology not only provides communications but also will provide monitoring, recording, storage, and display. IoMT in healthcare is used for measuring the vital signs of the human body, which allows medical professionals to assess the well-being of a patient. The doctor may recommend lifestyle modifications, prescribe more tests, or diagnose a disorder according to the results. This paper illustrates the remote-control health monitoring system (HMS) with the integration of a UAV, which allows the doctor to access the data and analyze the patient data remotely. Thus, the proposed HMS-UAV system aims to measure the temperature, humidity, blood pressure, heart rate, and SpO₂ and stores the data on the UAV. Several sensors were thus used namely DHT11, MAX30102, Myoware and K24C16, and the Raspberry Pi camera. Reduced hospital stays and avoidance of readmissions are benefits of remote patient monitoring with IoMT-based UAVs. Contrary to its advantages, IoMT has flaws in information processing since a huge volume of data are needed to be handled in a single environment. One major novel inclusion in this work is to measure multiple parameters and provide a comparative analysis for all of them. Furthermore, the functionality of video recorded and stored is included where the doctor can surveil the patient.

Diabetes is one of the costliest chronic diseases, it is a metabolic disorder in which a patient has excessive blood sugar levels due to the body's inability to create enough insulin, and it can also cause long-term harm to the heart, blood vessels, eyes, kidneys, and nerves. Adults with diabetes are twice as likely as non-diabetics to have a heart attack or stroke. Despite its massive impact on the global population, no kind of diabetes has a cure. Although most medications help patients manage their symptoms to some extent, diabetics nevertheless suffer several long-term health concerns. So, if we are able to predict diabetes early, we could control it and it can be done by using Machine learning techniques. Our work aim is to predict if the patient has diabetes using Machine learning techniques and the ensemble method. We will be using four algorithms which are SVM, KNN, Logistic Regression, and Random Forest classifier and we would also compare all four models to check which model is giving the best accuracy and link our best model to a web app that could predict if the patient has any chances of having diabetes.

This paper focuses on the implementation and performance comparison of a conventional and an intelligent method for estimation of SoC of a battery. Two different methods of estimation have been selected after careful study and literature review. The first method is Linear Kalman Filter (LKF), which is a conventional method, widely in use. The second method selected is Neural network using Feed Forward. The final results of both the methods are compared and studied to draw a conclusion. Both the methods have been implemented in MATLAB software. For Kalman Filter implementation, Thevenin circuit is modelled to achieve the needed equations. These equations are used to calculate the predict the error which the updates the Kalman gain. In Neural networks, the implementation comprises of training and testing. Mini batches have been taken for the training of the network along with Adam optimizer.

For a person who lost their arm or an upper limb, even a simple task becomes cumbersome because of their disability. Prosthetics play an important role in helping these people cope up with the challenges they face. Swift developments in technology have resulted in powered myoelectric hand prosthetics entering the market but are avoided by many for being expensive to purchase and maintain. This paper outlines the development of an economical prosthetic claw that can be controlled by muscle signals. The project primarily aims to bridge the gap between cheap non-functional prosthetics and expensive fully controllable prosthetics by being affordable, durable, and easy to manufacture without sacrificing functionality. The claw and its components have been designed to be easy to modify, repair, and replace, making it a flexible platform for customization as per the user’s need. This translates to an efficient and feasible solution to the ever-growing need for affordable functional upper limb prosthetics for the physically challenged.