Predicting and Reducing the Impact of Errors in Character-Based Text Entry

This dissertation focuses on the effect of errors in character-based text entry techniques. The effect of errors is targeted from theoretical, behavioral, and practical standpoints. This document starts with a review of the existing literature. It then presents results of a user study that investigated the effect of different error correction conditions on popular text entry performance metrics. Results showed that the way errors are handled has a significant effect on all frequently used error metrics. The outcomes also provided an understanding of how users notice and correct errors. Building on this, the dissertation then presents a new high-level and method-agnostic model for predicting the cost of error correction with a given text entry technique. Unlike the existing models, it accounts for both human and system factors and is general enough to be used with most character-based techniques. A user study verified the model through measuring the effects of a faulty keyboard on text entry performance. Subsequently, the work then explores the potential user adaptation to a gesture recognizer’s misrecognitions in two user studies. Results revealed that users gradually adapt to misrecognition errors by replacing the erroneous gestures with alternative ones, if available. Also, users adapt to a frequently misrecognized gesture faster if it occurs more frequently than the other error-prone gestures. Finally, this work presents a new hybrid approach to simulate pressure detection on standard touchscreens. The new approach combines the existing touch-point- and time-based methods. Results of two user studies showed that it can simulate pressure detection more reliably for at least two pressure levels: regular (~1 N) and extra (~3 N). Then, a new pressure-based text entry technique is presented that does not require tapping outside the virtual keyboard to reject an incorrect or unwanted prediction. Instead, the technique requires users to apply extra pressure for the tap on the next target key. The performance of the new technique was compared with the conventional technique in a user study. Results showed that for inputting short English phrases with 10% non-dictionary words, the new technique increases entry speed by 9% and decreases error rates by 25%. Also, most users (83%) favor the new technique over the conventional one. Together, the research presented in this dissertation gives more insight into on how errors affect text entry and also presents improved text entry methods

Optimizing Human Performance in Mobile Text Entry

Although text entry on mobile phones is abundant, research strives to achieve desktop typing performance "on the go". But how can researchers evaluate new and existing mobile text entry techniques? How can they ensure that evaluations are conducted in a consistent manner that facilitates comparison? What forms of input are possible on a mobile device? Do the audio and haptic feedback options with most touchscreen keyboards affect performance? What influences users' preference for one feedback or another? Can rearranging the characters and keys of a keyboard improve performance? This dissertation answers these questions and more. The developed TEMA software allows researchers to evaluate mobile text entry methods in an easy, detailed, and consistent manner. Many in academia and industry have adopted it. TEMA was used to evaluate a typical QWERTY keyboard with multiple options for audio and haptic feedback. Though feedback did not have a significant effect on performance, a survey revealed that users' choice of feedback is influenced by social and technical factors. Another study using TEMA showed that novice users entered text faster using a tapping technique than with a gesture or handwriting technique. This motivated rearranging the keys and characters to create a new keyboard, MIME, that would provide better performance for expert users. Data on character frequency and key selection times were gathered and used to design MIME. A longitudinal user study using TEMA revealed an entry speed of 17 wpm and a total error rate of 1.7% for MIME, compared to 23 wpm and 5.2% for QWERTY. Although MIME's entry speed did not surpass QWERTY's during the study, it is projected to do so after twelve hours of practice. MIME's error rate was consistently low and significantly lower than QWERTY's. In addition, participants found MIME more comfortable to use, with some reporting hand soreness after using QWERTY for extended periods

In-Air Un-Instrumented Pointing Performance

I present an analysis of in-air un-instrumented pointing and selection. I look at the performance of these systems and how this performance can be improved, with the eventual goal that their throughput reaches that of the mouse. Many potential limiting factors were explored, such as latency, selection reliability, and elbow stabilization. I found that the un-instrumented in-air pointing as currently implemented performed significantly worse, at less than 75% of mouse throughput. Yet, my research shows that this value can potentially reach mouse-like levels with lower system latencies, user training, and potentially improved finger tracking. Even without these improvements, the large range of applications for un-instrumented 3D hand tracking makes this technology still an attractive option for user interfaces

Interaction for Immersive Analytics

International audienceIn this chapter, we briefly review the development of natural user interfaces and discuss their role in providing human-computer interaction that is immersive in various ways. Then we examine some opportunities for how these technologies might be used to better support data analysis tasks. Specifically, we review and suggest some interaction design guidelines for immersive analytics. We also review some hardware setups for data visualization that are already archetypal. Finally, we look at some emerging system designs that suggest future directions

Eye Gaze Tracking for Human Computer Interaction

With a growing number of computer devices around us, and the increasing time we spend for interacting with such devices, we are strongly interested in finding new interaction methods which ease the use of computers or increase interaction efficiency. Eye tracking seems to be a promising technology to achieve this goal. This thesis researches interaction methods based on eye-tracking technology. After a discussion of the limitations of the eyes regarding accuracy and speed, including a general discussion on Fitts’ law, the thesis follows three different approaches on how to utilize eye tracking for computer input. The first approach researches eye gaze as pointing device in combination with a touch sensor for multimodal input and presents a method using a touch sensitive mouse. The second approach examines people’s ability to perform gestures with the eyes for computer input and the separation of gaze gestures from natural eye movements. The third approach deals with the information inherent in the movement of the eyes and its application to assist the user. The thesis presents a usability tool for recording of interaction and gaze activity. It also describes algorithms for reading detection. All approaches present results based on user studies conducted with prototypes developed for the purpose

Keyboard layout in eye gaze communication access: typical vs. ALS

The purpose of the current investigation was to determine which of three keyboard layouts is the most efficient for typical as well as neurologically-compromised first-time users of eye gaze access. All participants (16 neurotypical, 16 amyotrophic lateral sclerosis; ALS) demonstrated hearing and reading abilities sufficient to interact with all stimuli. Participants from each group answered questions about technology use and vision status. Participants with ALS also noted date of first disease-related symptoms, initial symptoms, and date of diagnosis. Once a speech generating device (SGD) with eye gaze access capabilities was calibrated to an individual participant's eyes, s/he practiced utilizing the access method. Then all participants spelled word, phrases, and a longer phrase on each of three keyboard layouts (i.e., standard QWERTY, alphabetic with highlighted vowels, frequency of occurrence). Accuracy of response, error rate, and eye typing time were determined for each participant for all layouts.  Results indicated that both groups shared equivalent experience with technology. Additionally, neurotypical adults typed more accurately than the ALS group on all keyboards. The ALS group made more errors in eye typing than the neurotypical participants, but accuracy and disease status were independent of one another. Although the neurotypical group had a higher efficiency ratio (i.e. accurate keystrokes to total active task time) for the frequency layout, there were no such differences noted for the QWERTY or alphabetic keyboards. No differences were observed between the groups for either typing rate or preference ratings on any keyboard, though most participants preferred the standard QWERTY layout. No relationships were identified between preference order of the three keyboards and efficiency scores or the quantitative variables (i.e., rate, accuracy, error scores). There was no relationship between time since ALS diagnosis and preference ratings for each of the three keyboard layouts.   It appears that individuals with spinal-onset ALS perform similarly to their neurotypical peers with respect to first-time use of eye gaze access for typing words and phrases on three different keyboard layouts. Ramifications of the results as well as future directions for research are discussed.  Ph.D

Integrating Usability Models into Pervasive Application Development

This thesis describes novel processes in two important areas of human-computer interaction (HCI) and demonstrates ways to combine these in appropriate ways. First, prototyping plays an essential role in the development of complex applications. This is especially true if a user-centred design process is followed. We describe and compare a set of existing toolkits and frameworks that support the development of prototypes in the area of pervasive computing. Based on these observations, we introduce the EIToolkit that allows the quick generation of mobile and pervasive applications, and approaches many issues found in previous works. Its application and use is demonstrated in several projects that base on the architecture and an implementation of the toolkit. Second, we present novel results and extensions in user modelling, specifically for predicting time to completion of tasks. We extended established concepts such as the Keystroke-Level Model to novel types of interaction with mobile devices, e.g. using optical markers and gestures. The design, creation, as well as a validation of this model are presented in some detail in order to show its use and usefulness for making usability predictions. The third part is concerned with the combination of both concepts, i.e. how to integrate user models into the design process of pervasive applications. We first examine current ways of developing and show generic approaches to this problem. This leads to a concrete implementation of such a solution. An innovative integrated development environment is provided that allows for quickly developing mobile applications, supports the automatic generation of user models, and helps in applying these models early in the design process. This can considerably ease the process of model creation and can replace some types of costly user studies.Diese Dissertation beschreibt neuartige Verfahren in zwei wichtigen Bereichen der Mensch-Maschine-Kommunikation und erläutert Wege, diese geeignet zu verknüpfen. Zum einen spielt die Entwicklung von Prototypen insbesondere bei der Verwendung von benutzerzentrierten Entwicklungsverfahren eine besondere Rolle. Es werden daher auf der einen Seite eine ganze Reihe vorhandener Arbeiten vorgestellt und verglichen, die die Entwicklung prototypischer Anwendungen speziell im Bereich des Pervasive Computing unterstützen. Ein eigener Satz an Werkzeugen und Komponenten wird präsentiert, der viele der herausgearbeiteten Nachteile und Probleme solcher existierender Projekte aufgreift und entsprechende Lösungen anbietet. Mehrere Beispiele und eigene Arbeiten werden beschrieben, die auf dieser Architektur basieren und entwickelt wurden. Auf der anderen Seite werden neue Forschungsergebnisse präsentiert, die Erweiterungen von Methoden in der Benutzermodellierung speziell im Bereich der Abschätzung von Interaktionszeiten beinhalten. Mit diesen in der Dissertation entwickelten Erweiterungen können etablierte Konzepte wie das Keystroke-Level Model auf aktuelle und neuartige Interaktionsmöglichkeiten mit mobilen Geräten angewandt werden. Der Entwurf, das Erstellen sowie eine Validierung der Ergebnisse dieser Erweiterungen werden detailliert dargestellt. Ein dritter Teil beschäftigt sich mit Möglichkeiten die beiden beschriebenen Konzepte, zum einen Prototypenentwicklung im Pervasive Computing und zum anderen Benutzermodellierung, geeignet zu kombinieren. Vorhandene Ansätze werden untersucht und generische Integrationsmöglichkeiten beschrieben. Dies führt zu konkreten Implementierungen solcher Lösungen zur Integration in vorhandene Umgebungen, als auch in Form einer eigenen Applikation spezialisiert auf die Entwicklung von Programmen für mobile Geräte. Sie erlaubt das schnelle Erstellen von Prototypen, unterstützt das automatische Erstellen spezialisierter Benutzermodelle und ermöglicht den Einsatz dieser Modelle früh im Entwicklungsprozess. Dies erleichtert die Anwendung solcher Modelle und kann Aufwand und Kosten für entsprechende Benutzerstudien einsparen