identification of human activity from video streaming smartphone data using intensified vgg16

Human activity recognition (har) techniques pick out and interpret human behaviors and actions by analyzing data gathered from various sensor devices. har aims to recognize and automatically categorize human activities using patterns and attributes taken from sensor data. har is complex in implementing the algorithm for a self-recorded dataset, including challenges such as age variation, wearing different clothes, environment and surface, the direction of the smartphone camera, and many more. the paper aims to propose a vgg16 deep learning framework including an activation function and different optimizers for classifying human activity from the real-time captured dataset; further, we compare the evaluated results with existing results. the proposed methods achieved 99.88% accuracy with excellent precision, recall, and f_measure values. comparing the evaluated result with existing outcomes over the wisdm and uci-har datasets. the new things in the article are a self-captured dataset of various aged male, female, and healthy volunteers to perform seven activities. furthermore, this research uses tensor processing units (tpu) available on kaggle to improve classification accuracy while reducing error rates and speeding up execution.