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| Publications [#386176] of Roberto Cabeza
search PubMed.Journal Articles
- Cia, I; Mendarte, AE; Garde, G; Cabeza, R; Almiron-Roig, E; Villanueva, A (2025). A Portable, Low-Cost Eye Tracking System for Predicting Visual Attention During Meal Ingestion. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 8(2). [doi]
(last updated on 2026/01/15)
Abstract: Obesity is a pressing public health challenge affecting physical and mental health. Understanding the cognitive and socio-economic factors that influence eating behaviors is essential for effective interventions. Visual attention is crucial in food choices, making it critical to study how individuals engage with food in real-life settings. However, current methodologies often rely on static images, limiting their ecological validity and real-world applicability, or are highly intrusive. This work offers a practical solution for tracking visual attention during real-life eating scenarios. We designed a tray with an integrated camera and a calibration pattern to remotely record volunteers during meal consumption. A convolutional neural network (CNN) was employed for the gaze tracking, utilizing a custom-built training database. Models without and with user calibration were provided. Results indicate an accuracy error of 5 cm, which improves to 2 cm with calibration, demonstrating our system’s effectiveness in measuring visual attention in food psychology experiments.
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