Hand-Eye: A Vision-Based Approach to Data Glove Calibration
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Bibliographic record
Abstract
We describe a new method for data glove calibration that uses computer vision techniques to create a filter for individual customization of input obtained from the glove. Our major observation is that it is possible to create a linear correlation between the hand posture reported by the data glove and the observed posture of the hand itself. We use a feature-based computer vision system independently to extract information about hand posture from video images of a human hand that is using the data glove. We simultaneously collect the glove data for the same posture. Linear regression is used on the combined sets of reported data to establish a filter that customizes the data reported from the glove for an individual user. The filtered glove data is mapped onto a computer-generated image of the hands skeletal structure. We show by comparison that the computer-generated hand image exhibits a posture quite similar to that of the actual hand.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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