A model-based fusion technique for ocular motion sensing
Bibliographic record
Abstract
Electrooculography (EOG) uses an electrical signal that records the cornea-retinal \ndifferential potential of the eyeball, which is linearly proportional to the eye movement. \nIn EOG, the electrodes placed in the outer canthus and lateral frontalis record \nthe horizontal and vertical eye movements. A main challenge in these signals is \nthat the eye movement information is compressed in the 0-35Hz range of the full \nspectrum of the recording (0-250/1000/2000Hz) depending on the dynamic range \nof the device. Moreover, these signals, like any other bio-signals, are contaminated \nheavily by artifacts and noises such as electroencephalography (EEG), eye-lid motions, \nillumination drift, skin drift, impedance drop of electrode, eye blinks, head \nmovements, and DC drift. Researchers have attempted to addressing these issues \nusing traditional techniques, including finite impulse response (FIR) filters, morphological \nfilters, time series motifs and wavelet-based approaches. However, most \nof these approaches have not been able to improve the performance significantly \nwithout compromising other features of the system such as the computational cost, \nreal-time performance and time lag. This thesis presents a model-based fusion \ntechnique for ocular motion sensing to improve the signal quality. The developed \napproach is tested with five different model-based approaches (Brownian, constant \nvelocity, constant acceleration, Westheimer, and linear reciprocal). Among these, \nthe approach based on the linear reciprocal human eye model show a significant \noverall improvement in the signal(500-700% improvement in SNR w.r.t. FIR and \n40-50% decrement in computational cost). Therefore, it is concluded that the approach \nbased on the linear reciprocal model provides the best fit in the implementation \nof high quality eye tracking systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".