Paper Title
Human Activity Recognition for Adult Monitoring

Abstract
The The Human Activity Recognition (HAR) technology is designed specifically for monitoring adults. Deep learning models are among the sophisticated machine learning methods used by the system. The objective is to precisely categorize and monitor a range of adult activities, including walking, falling, sleeping, standing, and sitting. With the help of an alert message function when a falling activity is detected an alert message is sent to the caretaker via email so that immediate actions could be taken. The suggested system has a lot of possibilities for applications in safety and healthcare that improve the wellbeing and monitoring of adults in many contexts. Here, we take a deep learning and machine learning methods using the datasets that are accessible to the public. The Human Activity Recognition System is a vital tool for keeping an eye on older people and making sure that early interventions are made in the event of any emergencies. To sum up, Human Activity Recognition System is a noteworthy development in adult surveillance technology. Its versatility, privacy-centric design, and precise recognition and classification of a broad range of activities make it a valuable and vital tool for enhancing adult well-being in a variety of circumstances. The Human Activity Recognition System is at the forefront of technological advancements meant to improve people's quality of life by means of perceptive and thoughtful monitoring systems. Keywords - Human Activity Recognition(HAR),Convolutional Neural Network(CNN).