Evaluating Cross-Subject and Cross-Device Consistency in Visual Fixation Prediction
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914339273834496 |
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| author | Wu, Yuli Konermann, Henning Mededovic, Emil Walter, Peter Stegmaier, Johannes |
| author_facet | Wu, Yuli Konermann, Henning Mededovic, Emil Walter, Peter Stegmaier, Johannes |
| contents | Understanding cross-subject and cross-device consistency in visual fixation prediction is essential for advancing eye-tracking applications, including visual attention modeling and neuroprosthetics. This study evaluates fixation consistency using an embedded eye tracker integrated into regular-sized glasses, comparing its performance with high-end standalone eye-tracking systems. Nine participants viewed 300 images from the MIT1003 dataset in subjective experiments, allowing us to analyze cross-device and cross-subject variations in fixation patterns with various evaluation metrics. Our findings indicate that average visual fixations can be reliably transferred across devices for relatively simple stimuli. However, individual-to-average consistency remains weak, highlighting the challenges of predicting individual fixations across devices. These results provide an empirical foundation for leveraging predicted average visual fixation data to enhance neuroprosthetic applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_05554 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Cross-Subject and Cross-Device Consistency in Visual Fixation Prediction Wu, Yuli Konermann, Henning Mededovic, Emil Walter, Peter Stegmaier, Johannes Human-Computer Interaction Understanding cross-subject and cross-device consistency in visual fixation prediction is essential for advancing eye-tracking applications, including visual attention modeling and neuroprosthetics. This study evaluates fixation consistency using an embedded eye tracker integrated into regular-sized glasses, comparing its performance with high-end standalone eye-tracking systems. Nine participants viewed 300 images from the MIT1003 dataset in subjective experiments, allowing us to analyze cross-device and cross-subject variations in fixation patterns with various evaluation metrics. Our findings indicate that average visual fixations can be reliably transferred across devices for relatively simple stimuli. However, individual-to-average consistency remains weak, highlighting the challenges of predicting individual fixations across devices. These results provide an empirical foundation for leveraging predicted average visual fixation data to enhance neuroprosthetic applications. |
| title | Evaluating Cross-Subject and Cross-Device Consistency in Visual Fixation Prediction |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2502.05554 |