Publication Date

6-27-2024

Document Type

Conference Proceeding

Publication Title

UMAP 2024 - Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization

DOI

10.1145/3631700.3665195

First Page

610

Last Page

615

Abstract

Objectives: This research delves into the dynamics of human-robot interaction (HRI) in retail environments, with a focus on robot detection from videos captured via an eye-tracking system. Methods: The study employs YOLOv8-nano model for real-time robot detection during grocery shopping tasks. All videos were processed using the YOLOv8 model to test inference speed while performing eye-tracking data analysis as a case study. Results: The YOLOv8 model demonstrated high precision in robot detection, with a mean average precision (mAP) of approximately 97.3% for Intersection over Union (IoU), 100% precision, and 99.87% recall for box detection. The model's ability to process an average of 160.36 frames per second (FPS) confirmed its suitability for real-time applications. In the case study on the impact of a robot's presence on human eye movements, the presence of a robot contributes to greater consistency in gaze fixation behavior, potentially leading to more predictable patterns of visual attention. Conclusion: The study's findings contribute significantly to the design of safer and more efficient cobot systems. They provide a deeper understanding of human responses in real-world scenarios, which is crucial for the development of effective HRI systems.

Funding Number

2132936

Funding Sponsor

National Science Foundation

Keywords

Applied Computing, Human Robot Interaction, Object Detection, Physiological Patterns

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Industrial and Systems Engineering

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