Finger contact keyboard for typing with tiny movement recognition
Bibliographic record
Abstract
Hemiplegic patients often struggle with typing rapidly and accurately using a standard keyboard. This study developed a keyboard in continuous contact with the fingers, allowing for easier and more effective typing. With the proposed keyboard, the user types with their healthy hand and paralyzed hand. The hardware and software of the finger contact keyboard were investigated. In deciding the hardware, we measured the hand shape, finger speed, and muscle fatigue using electromyography, magnetic positioning sensors, and force sensors for eight participants. Using the Pareto solution, we optimized the keyboard’s structure to maximize finger movement speed and minimize muscle fatigue. As the software of the proposed keyboard, we developed an algorithm implementing a neural network to identify intentional typing and tested the algorithm on five participants. The highest average discrimination accuracy was 99.3% when the force threshold was approximately 1.32 N. The mean accuracy achieved using the neural network was 90.8%, which is higher than that achieved using the threshold algorithm (80.4%). In 40 trials, the proposed keyboard achieved the same accuracy and speed as the standard keyboard, and the input time for a patient with hemiplegia was reduced by 16.2%.
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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 | 0.002 |
| 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) | 0.003 | 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 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".