Multi-Modal Fusion Techniques for Human Activity Recognition in Smart Environments

Abstract

Human activity recognition plays a vital role in various applications, including healthcare monitoring, security surveillance, and assistive technologies. This paper explores multi-modal fusion techniques for integrating data from different sensors to improve the accuracy and robustness of human activity recognition systems in smart environments. We investigate fusion strategies, feature representations, and model architectures, highlighting their impact on performance.