HUMAN ACTIVITY RECOGNITION SYSTEMS: METHODS AND APPLICATIONS

Authors: 

SANNIDI SHETTY

Modern Education Society's D. G. Ruparel College of Arts, Science and Commerce

Matunga West, Mumbai

Sannidishetty1712@gmail.com

Abstract

Human Activity Recognition (HAR) has become a vital area of research in artificial intelligence and machine learning, enabling systems to automatically identify and classify human actions from sensor data, video streams, or wearable devices. HAR plays a crucial role in domains such as healthcare, smart homes, surveillance, sports analytics, and human-computer interaction. Unlike traditional rule-based approaches, modern HAR systems leverage machine learning and deep learning techniques to handle variability, uncertainty, and complex temporal patterns in human behaviour.

This paper explores the foundations of HAR, including classical machine learning methods, deep learning architectures, and hybrid models. It examines the role of sensor fusion, feature extraction, and sequential modelling in improving recognition accuracy. A comparative analysis between conventional statistical approaches and advanced deep learning frameworks highlights improvements in adaptability, robustness, and scalability.

Challenges such as data diversity, real-time processing, privacy concerns, and generalisation across different populations are discussed. The study also outlines recent advances, including transformer-based models, multimodal learning, and edge computing integration. Findings suggest that HAR systems are increasingly capable of supporting intelligent applications in dynamic environments, with future research focusing on personalised recognition, privacy-preserving algorithms, and cross-domain adaptability.