Predictive text-entry in immersive environments

Virtual Reality (VR) has progressed significantly since its conception, enabling previously impossible applications such as virtual prototyping, telepresence, and augmented reality However, text-entry remains a difficult problem for immersive environments (Bowman et al, 2001b, Mine et al , 1997). Wearing a head-mounted display (HMD) and datagloves affords a wealth of new interaction techniques. However, users no longer have access to traditional input devices such as a keyboard. Although VR allows for more natural interfaces, there is still a need for simple, yet effective, data-entry techniques. Examples include communicating in a collaborative environment, accessing system commands, or leaving an annotation for a designer m an architectural walkthrough (Bowman et al, 2001b). This thesis presents the design, implementation, and evaluation of a predictive text-entry technique for immersive environments which combines 5DT datagloves, a graphically represented keyboard, and a predictive spelling paradigm. It evaluates the fundamental factors affecting the use of such a technique. These include keyboard layout, prediction accuracy, gesture recognition, and interaction techniques. Finally, it details the results of user experiments, and provides a set of recommendations for the future use of such a technique in immersive environments

An Ambiguous Technique for Nonvisual Text Entry

Text entry is a common daily task for many people, but it can be a challenge for people with visual impairments when using virtual touchscreen keyboards that lack physical key boundaries. In this thesis, we investigate using a small number of gestures to select from groups of characters to remove most or all dependence on touch locations. We leverage a predictive language model to select the most likely characters from the selected groups once a user completes each word. Using a preliminary interface with six groups of characters based on a Qwerty keyboard, we find that users are able to enter text with no visual feedback at 19.1 words per minute (WPM) with a 2.1% character error rate (CER) after five hours of practice. We explore ways to optimize the ambiguous groups to reduce the number of disambiguation errors. We develop a novel interface named FlexType with four character groups instead of six in order to remove all remaining location dependence and enable one-handed input. We compare optimized groups with and without constraining the group assignments to alphabetical order in a user study. We find that users enter text with no visual feedback at 12.0 WPM with a 2.0% CER using the constrained groups after four hours of practice. There was no significant difference from the unconstrained groups. We improve FlexType based on user feedback and tune the recognition algorithm parameters based on the study data. We conduct an interview study with 12 blind users to assess the challenges they encounter while entering text and solicit feedback on FlexType, and we further incorporate this feedback into the interface. We evaluate the improved interface in a longitudinal study with 12 blind participants. On average, participants entered text at 8.2 words per minute using FlexType, 7.5 words per minute using a Qwerty keyboard with VoiceOver, and at 26.9 words per minute using Braille Screen Input

Designing Text Entry Methods for Non-Verbal Vocal Input

Katedra počítačové grafiky a interakc

A concept-environment for computer-based augmentative and alternative communication founded on a systematic review

Introduction: locked-In Syndrome is admittedly the worst case of motor and speech impairment, it seriously damages the ability of oral and gestural communication of patients. In recent years, alternative and augmentative communication technology has provided resources to restore these patients' ability to communicate. Methods: in order to relate and classify the main methods with that purpose, this work conducted a systematic review on several journal databases. Results: we found 203 related papers and 55 of them were selected to compose the study. After that, we classified them into three major groups and we identified the main difficulties when using each approach. Conclusion: in order to overcome these difficulties, we propose a new system concept to develop an adaptive, robust and low cost communication environment. The proposed system is composed of five modules: data entry, communication, aid to the caregiver and external interaction

Portable product miniaturization and the ergonomic threshold

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1997.Includes bibliographical references (p. 124-125).by David H. Levy.Ph.D

The development and evaluation of gaze selection techniques

Eye gaze interaction enables users to interact with computers using their eyes. A wide variety of eye gaze interaction techniques have been developed to support this type of interaction. Gaze selection techniques, a class of eye gaze interaction techniques which support target selection, are the subject of this research. Researchers developing these techniques face a number of challenges. The most significant challenge is the limited accuracy of eye tracking equipment (due to the properties of the human eye). The design of gaze selection techniques is dominated by this constraint. Despite decades of research, existing techniques are still significantly less accurate than the mouse. A recently developed technique, EyePoint, represents the state of the art in gaze selection techniques. EyePoint combines gaze input with keyboard input. Evaluation results for this technique are encouraging, but accuracy is still a concern. Early trigger errors, resulting from users triggering a selection before looking at the intended target, were found to be the most commonly occurring errors for this technique. The primary goal of this research was to improve the usability of gaze selection techniques. In order to achieve this goal, novel gaze selection techniques were developed. New techniques were developed by combining elements of existing techniques in novel ways. Seven novel gaze selection techniques were developed. Three of these techniques were selected for evaluation. A software framework was developed for implementing and evaluating gaze selection techniques. This framework was used to implement the gaze selection techniques developed during this research. Implementing and evaluating all of the techniques using a common framework ensured consistency when comparing the techniques. The novel techniques which were developed were evaluated against EyePoint and the mouse using the framework. The three novel techniques evaluated were named TargetPoint, StaggerPoint and ScanPoint. TargetPoint combines motor space expansion with a visual feedback highlight whereas the StaggerPoint and TargetPoint designs explore novel approaches to target selection disambiguation. A usability evaluation of the three novel techniques alongside EyePoint and the mouse revealed some interesting trends. TargetPoint was found to be more usable and accurate than EyePoint. This novel technique also proved more popular with test participants. One aspect of TargetPoint which proved particularly popular was the visual feedback highlight, a feature which was found to be a more effective method of combating early trigger errors than existing approaches. StaggerPoint was more efficient than EyePoint, but was less effective and satisfying. ScanPoint was the least popular technique. The benefits of providing a visual feedback highlight and test participants' positive views thereof contradict views expressed in existing research regarding the usability of visual feedback. These results have implications for the design of future gaze selection techniques. A set of design principles was developed for designing new gaze selection techniques. The designers of gaze selection techniques can benefit from these design principles by applying them to their technique

Modèle théorique et outil de simulation pour une meilleure évaluation des claviers logiciels augmentés d'un système de prédiction de mots

Les claviers logiciels se sont démocratisés pour rendre possible la saisie de textes en mobilité sur des dispositifs dépourvus de claviers physiques tels que les téléphones portables nouvelle génération. Cependant, ces claviers présentent plusieurs inconvénients comme la lenteur de la saisie et la fatigue engendrées pour les utilisateurs déficients moteurs. La solution intuitive était d'allier ces logiciels à des listes contenant les mots susceptibles de continuer la saisie d'un mot initié par l'utilisateur. Bien que ces listes, dites listes de prédiction, réduisent le nombre de clics et le nombre d'opérations, la vitesse de saisie de l'utilisateur a diminué. Une expérimentation outillée d'un système de suivi du regard a ainsi permis de déterminer des " stratégies " de fonctionnement de l'utilisateur face à une liste de mots. Ces résultats ont ainsi permis d'affiner les modèles de prédiction de manière à réduire l'écart séparant les performances prédites des performances réellement enregistrées. A partir des constats effectués lors de la première expérimentation, nous proposons deux variantes de l'utilisation des listes de prédiction de mots. La première propose un nouveau moyen d'interagir avec la liste de mots et permet ainsi de maximiser l'utilisation de celle-ci. La seconde évalue un repositionnement de la liste de mots de manière à réduire le nombre de mouvements oculaires vers la liste. Ces deux évolutions, évaluées théoriquement puis au moyen d'une expérimentation utilisateur, permettent ainsi d'améliorer les performances de saisie par rapport à une liste de prédiction de mots classique.Predictive model and simulation tool for a best evaluation of soft keyboard augmented by words prediction list The software keyboards are used to enable text input in mobility and for devices without physical keyboards, such as the new generation of mobile phones. However, these keyboards have several drawbacks such as slowness text entry and fatigue generated for motor impaired users. The solution was to combine software keyboard to lists containing the words likely to continue the word introduced by the user. While these lists, so-called prediction lists, reduce the number of clicks and the number of operations, the speed of user input has decreased. An experiment with an eye tracking system has identified the "strategies" of the user while using and searching a list of words. These results were helpful to refine the prediction models in order to reduce the gap between the performance predicted and the performance actually recorded. Based on observations made during the first experiment, we propose two variants of the use of word prediction list. The first proposes a new way to interact with the list of words and allows maximum use of it. The second evaluates a repositioning of the list of words in order to reduce the number of eye movements to the list. These two propositions were theoretically and experimentally evaluated by users. These software can improve the input performances compared with a classic word prediction list