Analysis of Computational Model for Detection and Recognition of Human Activity Using Deep Learning

The proliferation of sensors and wearable technology in recent years has produced enormous volumes of sensory data about human activity. Effective data analysis is essential for many applications, including security, sports, and healthcare. The goal of this research is to use a deep learning approach to develop a fast and reliable method for identifying human activities from sensory datasets. The suggested approach makes use of GPU parallel processing power to improve the speed and effectiveness of activity identification algorithms. A deep constitutional neural network architecture called the VGG16 (Visual Geometry Group 16) is intended for image categorization. There are three fully connected layers and thirteen constitutional layers among its sixteen weight layers. VGG16 is renowned for its efficiency and simplicity, demonstrating competitive performance across a range of image recognition applications. The high-level properties that the network learnt during training, which allowed it to identify patterns and objects in pictures, are referred to as the abstract representation. The tasks of image categorization, object recognition, and feature extraction in computer vision applications include but are not limited to healthcare image analysis.