Design of miniaturized coil system using MEMS technology for eye movement measurement
Bibliographic record
Abstract
Application of eye movement measurement is especially significant in Neuroscience. Results from eye tracking can give valuable insight into the correlation between neural activity and eye movements. Abnormalities in the eye movements also provide information regarding diagnosis and progress of neurological diseases such as dementia. Another application is in developing human-computer interfaces as a means of communication for the severely handicapped. Although there are numerous eye tracking techniques available, the magnetic search coil method has been prominently used by researchers due to its high accuracy and precision. This technique typically requires the subjects head to be fixed for accurate measurements due to the use of large field coils. Here we propose a miniaturized coil system using MEMS technology for application in eye movement measurement. The coils were designed and modeled using CoventorWare and MagNet software. The microcoils were then fabricated in the McGill Nanotools microfabrication laboratory. We present the results for using the materials Indium Tin Oxide (ITO) and Aluminium for the fabrication of coils. We found that the resulting coil system is capable of identifying displacements along the X, Y and Z axis. The resolution of the system depends on the configuration of the system, it was calculated to be around 20-40 μm on the plane of the coil and it increases near the centre of the coil. Although the proposed coil system holds significant potential, but further exhaustive testing needs to be performed in an environment simulating eye movements.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".