Assessing Human Visual Attention in Retail Human-Robot Interaction: A YOLOv8-Nano and Eye-Tracking Approach
Kamlesh Kumar, Yuhao Chen, Boyi Hu, Yue Gang Luo · 2024
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.