Automatic Human Activity Detection Using Novel Deep Learning Architecture

Human Activity Detection (HAD) refers to the process of categorizing and identifying human motion. HAD studies have received significant attention in the estimation of frequency and duration. This has facilitated the use of sophisticated assistive technologies and significant manual examination. The artificial intelligence used by HAD enables experts to deduce human behavior based on data obtained from sources like wearables or physical objects. Deep tissue massage is becoming more often used for HAD (healthcare-associated infections), particularly in relation to everyday tasks and functions. HAD primarily focuses on discerning activities such as ambulation, locomotion, leaping, and engaging in recreational activities. As part of this research project, a new Convolutional Neural Network framework for HAD has been developed. The results of the experiments show that the proposed model performs better than traditional machine learning techniques in correctly recognizing and classifying human actions.