Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.03708 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915302432833536 |
|---|---|
| author | Raju, Mehedi Hasan Aziz, Samantha Proulx, Michael J. Komogortsev, Oleg V. |
| author_facet | Raju, Mehedi Hasan Aziz, Samantha Proulx, Michael J. Komogortsev, Oleg V. |
| contents | We present a real-time gaze-based interaction simulation methodology using an offline dataset to evaluate the eye-tracking signal quality. This study employs three fundamental eye-movement classification algorithms to identify physiological fixations from the eye-tracking data. We introduce the Rank-1 fixation selection approach to identify the most stable fixation period nearest to a target, referred to as the trigger-event. Our evaluation explores how varying constraints impact the definition of trigger-events and evaluates the eye-tracking signal quality of defined trigger-events. Results show that while the dispersion threshold-based algorithm identifies trigger-events more accurately, the Kalman filter-based classification algorithm performs better in eye-tracking signal quality, as demonstrated through a user-centric quality assessment using user- and error-percentile tiers. Despite median user-level performance showing minor differences across algorithms, significant variability in signal quality across participants highlights the importance of algorithm selection to ensure system reliability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_03708 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating Eye Tracking Signal Quality with Real-time Gaze Interaction Simulation Raju, Mehedi Hasan Aziz, Samantha Proulx, Michael J. Komogortsev, Oleg V. Human-Computer Interaction We present a real-time gaze-based interaction simulation methodology using an offline dataset to evaluate the eye-tracking signal quality. This study employs three fundamental eye-movement classification algorithms to identify physiological fixations from the eye-tracking data. We introduce the Rank-1 fixation selection approach to identify the most stable fixation period nearest to a target, referred to as the trigger-event. Our evaluation explores how varying constraints impact the definition of trigger-events and evaluates the eye-tracking signal quality of defined trigger-events. Results show that while the dispersion threshold-based algorithm identifies trigger-events more accurately, the Kalman filter-based classification algorithm performs better in eye-tracking signal quality, as demonstrated through a user-centric quality assessment using user- and error-percentile tiers. Despite median user-level performance showing minor differences across algorithms, significant variability in signal quality across participants highlights the importance of algorithm selection to ensure system reliability. |
| title | Evaluating Eye Tracking Signal Quality with Real-time Gaze Interaction Simulation |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2411.03708 |