Advanced Human Activity Recognition With Enhanced Convolutional Neural Networks

Abstract

Human Activity Recognition (HAR) serves as a pivotal function with implications spanning from healthcare monitoring to security systems. Recent advancements in Machine Learning (ML) alongside Computer Vision techniques have demonstrated considerable progress in automating this task. This paper offers a detailed review and analysis of diverse ML algorithms and Computer Vision methods used in HAR systems. We explore the challenges encountered in this field, such as variability in human actions, occlusion, and changes in perspective, and examine how various methodologies mitigate these issues. Moreover, we spotlight principal datasets employed for training and evaluation. Through a thorough empirical analysis, we assess the performance of various ML models in precisely identifying human activities from sensor data or video feeds. Our observations affirm the effectiveness of deep learning frameworks, especially Convolutional Neural Networks (CNNs), in detecting complex spatiotemporal patterns essential for HAR endeavors. Additionally, we explore forthcoming trends, ongoing challenges, and future avenues for research in this evolving area, highlighting the promise for continued progress through joint efforts among the ML and Computer Vision communities.v