A comparative analysis of the health monitoring process using deep learning methods for brain tumour

Abstract

The use of Internet of Things (IoT) devices has been growing rapidly recently. As technology improves, products for older people are developed in the health industry. Applications for virtual and remote interactions with patients are somewhat too simple to use. If IoT technology is used well, it may be possible to treat physically erratic individuals without having to see a doctor often. As a result of this research, a prototype of an Internet of Things–based remote health monitoring system for senior patients has been developed. The suggested technique enables the care to better manage and keep an eye on the well-being of older patients. The system will design and implement efficient contact with the patient's families. This model has a number of sensors, including sensors for arthritis, body temperature, skin response, and pulse. Each sensor is paired with a system of proposals for analysis and validation. The data feasibility of the data obtained from the IoT sensors of the proposed system efficacy is being explored. The information obtained from the sensors and the extracted data is sent to cloud storage via distributed storage. In the performance studies, the efficacy of the proposed system is evaluated based on the data retrieved and used against certain health metrics like heartbeat and temperature sensors. IoT combined with wellness wearables may eliminate the need to visit a doctor for urgent health conditions. To ensure data accuracy & system scaling, Internet of Things devices are employed in the proposed system, & the power consumption and battery life are analysed.