Working with a computer hands-free using Nouse ® Perceptual Vision Interface
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,027 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».