Human Activity Recognition: A Review of RFID and Wearable Sensor Technologies Powered by AI

Abstract

Human activity recognition (HAR) has garnered significant attention across diverse domains such as fitness enhancement, safety, elderly care, clinical monitoring, and smart environments. However, despite its potential, HAR faces challenges like handling noisy and diverse data, ensuring real-time performance, maintaining user privacy, and achieving high accuracy across varying contexts and activities. A primary challenge of HAR lies in maintaining consistency and accuracy during data collection amidst varied activities and environments. This review article provides a comprehensive overview of the advancements in AI-enhanced HAR methods, with a focus on radio frequency identification system, wearable devices, and smartphone sensor technologies. We delve into the frameworks of these technologies, detailing processes like data collection, preprocessing, and the application of machine learning and deep learning algorithms. Additionally, we outline the advantages and drawbacks of these techniques and provide a brief comparison between them.