Human Activity Recognition using Deep Learning

Abstract

Machine learning research is heavily focused on human activity detection since it has various applications in a variety of fields, including security, entertainment, ambient supported living, and health management and monitoring. Researchers' interest in human daily activities is seen from studies on human activity recognition (HAR). As a result, the general architecture of the HAR system and a description of its key elements are described in this work. Review of the state-of-the-art in accelerometer-based human activity recognition According to this survey, the majority of recent research that employed CNN for HAR identification relied on it even though other deep learning models also showed acceptable accuracy. The paper suggests a 2 different classification depending on the kind of machine learning (conventional or deep learning) and the manner of execution (online or offline). Comparing 48 studies prediction performance, algorithms, activity categories, and used equipment. The study concludes by contrasting the difficulties and problems associated with identifying human movement based on accelerometer sensors utilizing deep learning versus conventional machine learning, as well as online versus offline.