Working with a computer hands-free using Nouse ® Perceptual Vision Interface
Bibliographic record
Abstract
Normal work with a computer implies being able to perform the following three computer control tasks: 1) pointing , 2) clicking, and 3) typing. Many attempts have been made to make it possible to perform these tasks hands-free using a video image of the user as input. Nevertherless, rehabilitation center practitioners agree that no marketable solution making vision-based hands-free computer control a commonplace reality for disabled users has been produced as of yet. as reported by rehabilitation center practitioners, no marketable solution making vision-based hands-free computer control a commonplace reality for disabled users has been produced as of yet. Here we present the Nouse Perceptual Vision Interface (Nouse PVI) that is hoped to finally offer a solution to a long-awaited dream of many disabled users. Evolved from the original Nouse 'Nose as Mouse' concept and currently under testing with EBRI 1, Nouse PVI has several unique features that make it preferable to other hands-free vision-based computer input alternatives. First, its original idea of using the nose tip as a single reference point to control a computer has been confirmed to be very convenient for disabled users. For them the nose literally becomes a new 'finger' which they can use to write words, move a cursor on screen, click or type. Being able to track the nose tip with subpixel precision within a wide range of head motion, makes performing all control tasks possible. Its second main feature is a feedback-providing mechanism that is implemented using a concept of Perceptual Nouse Cursor (Nousor) which creates an invisible link between the computer and user and which is very important for control as it allows the user to adjust his/her head motion so that the computer can better interpret them.Finally, there are a number of design solutions related specifically tailored for vision-based data entry using small range head motion such as motion codes(NouseCode), a motion-based virtual keyboard (Nouse-Board and NousePad) and a word-by-word letter drawing tool (NouseChalk). While presenting the demonstrations of these innovative tools, we also address the issue of the user's ability and readiness to work with a computer in the brand new way - i.e. hands-free. The problem is that a user has to understand that it is not entirely the responsibility of a computer to understand what one wants, but it's also the responsibility of the user to make sure that the computer understands what the user motions mean. Just as a conventional computer user cannot move the cursor on the screen without first putting his or her hand on the mouse, a perceptual interface user cannot work with a computer until he or she 'connects' to it. That is, the computer and user must work as a team for the best control results to be achieved. This presentation therefore is designed to serve both as a guide to those developing vision-based input devices and as a tutorial for those who will be using them.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".