Energy-efficient, low-latency, and non-contact eye blink detection with capacitive sensing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work described a novel non-contact, wearable, real-time eye blink detection solution based on capacitive sensing technology. A custom-built prototype employing low-cost and low-power consumption capacitive sensors was integrated into standard glasses, with a copper tape electrode affixed to the frame. The blink of an eye induces a variation in capacitance between the electrode and the eyelid, thereby generating a distinctive capacitance-related signal. By analyzing this signal, eye blink activity can be accurately identified. The effectiveness and reliability of the proposed solution were evaluated through five distinct scenarios involving eight participants. Utilizing a user-dependent detection method with a customized predefined threshold value, an average precision of 92% and a recall of 94% were achieved. Furthermore, an efficient user-independent model based on the two-bit precision decision tree was further applied, yielding an average precision of 80% and an average recall of 81%. These results demonstrate the potential of the proposed technology for real-world applications requiring precise and unobtrusive eye blink detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it