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
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Industrial and Systems Engineering
Recommended Citation
Kamlesh Kumar, Yuhao Chen, Boyi Hu, and Yue Luo. "Assessing Human Visual Attention in Retail Human-Robot Interaction: A YOLOv8-Nano and Eye-Tracking Approach" UMAP 2024 - Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (2024): 610-615. https://doi.org/10.1145/3631700.3665195