Deep Neural Networks for Human Activity Recognition and Monitoring

Abstract

Human Activity Recognition (HAR) and monitoring have become increasingly important in various fields, including healthcare, fitness, and smart environments. Deep neural networks (DNNs) have shown great promise in accurately classifying human activities from sensor data, enabling real-time monitoring of individuals' movements and behaviors. This article explores the application of DNNs in human activity recognition, with a focus on sensor-based data, model architectures, and real-time monitoring systems. It also discusses the challenges and future directions of HAR systems, emphasizing the role of DNNs in advancing personalized health monitoring and smart environments.

Human Activity Recognition (HAR) and monitoring have become increasingly important in various fields, including healthcare, fitness, and smart environments. Deep neural networks (DNNs) have shown great promise in accurately classifying human activities from sensor data, enabling real-time monitoring of individuals' movements and behaviors. This article explores the application of DNNs in human activity recognition, with a focus on sensor-based data, model architectures, and real-time monitoring systems. It also discusses the challenges and future directions of HAR systems, emphasizing the role of DNNs in advancing personalized health monitoring and smart environments.