Search papers, labs, and topics across Lattice.
This study introduces a structured protocol for creating a dynamic multimodal dataset aimed at analyzing user engagement in human-robot interactions (HRI), incorporating not only observable behavioral cues but also physiological signals and self-reports. By integrating these diverse data sources, the research addresses the limitations of previous studies that primarily focused on behavioral observations. The key finding is that varying task complexity significantly influences engagement metrics, highlighting the need for a comprehensive approach to understanding HRI dynamics.
Integrating physiological signals with behavioral data reveals that task complexity dramatically alters user engagement in human-robot interactions.
This paper presents an experimental design for constructing a multimodal dataset to analyze user engagement in human-robot interaction (HRI). Prior studies have mainly relied on observable behavioral cues, with limited frameworks integrating physiological signals. We therefore propose a structured data-collection protocol to build a multimodal dataset that includes wearable physiological signals, behavioral data, and self-report measures under different levels of task complexity defined in this experiment.
