A Survey On Human Activity Detection in Patient Monitoring

Abstract

Human activity detection, a burgeoning field in computer science and artificial intelligence, aims to develop intelligent systems capable of recognizing and categorizing human actions and behaviors from sensor data or video streams. This research addresses a critical need for applications in various domains, including healthcare, security, sports analysis, and human-computer interaction. The overarching goal is to enhance our understanding of human behavior, automate surveillance, improve healthcare monitoring, and enable more intuitive human-machine interfaces. Efforts in human activity detection encompass data collection through sensors or cameras, preprocessing techniques to filter and enhanced at a quality, and feature extraction to represent meaningful patterns in the data. Machine learning and deep learning algorithms play a pivotal role in training models to classify and recognize activities accurately. As human activity detection advances, it holds the promise of revolutionizing fields such as remote patient monitoring, smart homes, security surveillance, and immersive gaming experiences.