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Graph-Based Methods for Multimodal Indoor Activity Recognition: A Comprehensive Survey

Authors:

Saeedeh Javadi, Daniele Riboni , Luigi Borzi, Samaneh Zolfaghari

Research group:

Health-promoting engineering

Publication Type:

Journal article

Venue:

IEEE Transactions on Computational Social Systems

Publisher:

IEEE

DOI:

10.1109/TCSS.2024.3523240

Abstract

This survey article explores graph-based approaches to multimodal human activity recognition in indoor environments, emphasizing their relevance to advancing multimodal representation and reasoning. With the growing importance of integrating diverse data sources such as sensor events, contextual information, and spatial data, effective human activity recognition methods are essential for applications in smart homes, digital health, and more. We review various graph-based techniques, highlighting their strengths in encoding complex relationships and improving activity recognition performance. Furthermore, we discuss the computational efficiencies and generalization capabilities of these methods across different environments. By providing a comprehensive overview of the state-of-the-art in graph-based human activity recognition, this article aims to contribute to the development of more accurate, interpretable, and robust multimodal systems for understanding human activities in indoor settings.

Bibtex

@article{Javadi7133,
author = {Saeedeh Javadi and Daniele Riboni and Luigi Borzi and Samaneh Zolfaghari},
title = {Graph-Based Methods for Multimodal Indoor Activity Recognition: A Comprehensive Survey},
month = {January},
year = {2025},
journal = {IEEE Transactions on Computational Social Systems},
publisher = {IEEE},
url = {http://www.es.mdu.se/publications/7133-}
}