Text input in Smartwatches Based Gestures Using Geometric Shape
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a method for text input based on gestures to be used in smartwatches using geometric shapes. To make the recognition of gestures, we used the incremental recognition algorithm gestures. a template with straight curves were developed using the reduced equation of the circle. Using this template, thirty users have entered all the letters of the alphabet three times each in three groups totaling ninety inserts for each letter. Gestures entered by users have been used to train a Naive Bayes classifier that calculates the probability of insertion for each letter to from the user-entered gestures. During the development work was also carried out a study of the most frequent letters of the Portuguese language. Another partial result of the project is a prototype in which the user enters all the letters of the alphabet using the template gestures. By the time the user enters a gesture prototype automatically suggests the most frequent letters using the Naïve Bayes classifier.
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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 | 0.001 | 0.001 |
| 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 it