A Keystroke and Pointer Control Input Interface for Wearable Computers
Bibliographic record
Abstract
The widespread adoption of mobile electronic devices and the advent of wearable computing have encouraged the development of compact alternatives to the keyboard and mouse. These include one-handed keyboards, digitizing tablets, and glove-based devices. This paper describes a combination pointer position and non-chorded keystroke input device that relies on miniature wrist-worn wireless video cameras that track finger position. A hidden Markov model is used to correlate finger movements to keystrokes during a brief training phase, after which the user can type in the air or above a flat surface as if typing on a standard keyboard. Language statistics are used to help disambiguate keystrokes, allowing the assignment of multiple unique keys to each finger and obviating chorded input. In addition, the system can be trained to recognize certain finger positions for switching between input modes; for example, from typing mode to pointer movement mode. In the latter mode of operation, the position of the mouse pointer is controlled by hand movement. The camera motion is estimated by tracking environmental features and is used to control pointer position. This allows fast switching between keystroke mode and pointer control mode.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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 source (direct Gemma or distilled Codex), 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".