An Edge-Fog-Cloud-based Hierarchical Adaptive Model for Human-Robot Interaction*

With the rapid advancement of intelligent devices and the Internet of Things (IoT), a vast amount of detailed information can now be captured and analyzed from the human body. This progress has led to the development of more comprehensive and versatile Body Area Networks (BANs). Despite this promising landscape, the field lacks mature global applications, and numerous challenges await resolution, particularly in Human-Robot Interaction (HRI).To address the limitations of HRI, this paper focuses on recognizing complex human activities and achieving rapid, stable transmission of high-load data. An Edge-Fog-Cloud-based hierarchical adaptive system is proposed, utilizing multiple IMU sensors for human activity recognition (HAR). The system introduces an adaptive strategy to enhance the accuracy of identifying complex human behavior. By incorporating hierarchical communication and classification methods, the system improves the communication network’s real-time performance and the algorithms’ architectural structure, ensuring real-time capabilities for the HRI system.