Survey on Multimodal Complex Human Activity Recognition

Document Type : Original Article

Authors

1 Software Engineering, FCAI Helwan University, Cairo, Egypt

2 faculty of computer and Aritifal intelligence Helwan university

3 Information Systems Dept.

4 Faculty of Computers & Artificial Intelligence

5 Helwan University (HU)

6 Ain Helwan - Cairo 11795 - Egypt

7 HCI-lab, Faculty of Computers & Artificial Intelligence, Helwan University

Abstract

Multimodal complex human activity recognition involves the recognition and understanding of human activities using multiple modalities, such as visual, auditory, and sensor-based data. With the proliferation of smart devices and the increasing availability of multimodal data, there is a growing need for robust and efficient methods to recognize complex human activities in real-world settings. This paper presents an overview of multimodal complex human activity recognition techniques, highlighting the challenges and recent advancements in the field. This paper discusses the fusion of multimodal data sources, including visual and audio cues, as well as sensor data from wearable devices or environmental sensors. Furthermore, it explores the use of machine learning and deep learning algorithms for activity recognition and the used datasets in this field. Overall, this paper provides a comprehensive overview of techniques, challenges, and future directions in multimodal complex human activity recognition, aiming to stimulate further research in this exciting and rapidly evolving field.

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