Eecs

Technical advancements involving the pervasive cell phone make it a viable computing platform. As such, the input of textual information is vital. Use of ambiguous keypads preserves the device's portable nature by mapping multiple letters to a single key. Metrics exist to assess such keypads, but they do not account for the physical resources employed by keypads, or for the perceptual and cognitive load placed on the user. Furthermore, existing tools typically encapsulate only one metric, making detailed evaluation cumbersome and time-consuming. This thesis outlines a new metric that quantifies a keypad's efficiency using the number of text entry keys it employs. It also presents a revised performance model that incorporates perceptual and cognitive timing values to reflect actual practice. Encompassing both contributions is an original toolkit that simplifies and streamlines analysis of ambiguous keypads. v ACKNOWLEDGEMENTS I would like to convey my sincerest gratitude to my supervisor, Dr. Scott MacKenzie, for his knowledge, guidance, and support. His generous funding of me via his NSERC grant made this research possible. Thanks also go to Dr. Wolfgang Stuerzlinger, who introduced me to the graduate program, Dr. Bil Tzerpos, and Dr. Stephen Chen for taking time out of their busy schedules to serve on my thesis examination committee. In addition, thanks go to Chris Klochek, who served as a preliminary participant for the evaluation described in Chapter 3, and to Andriy Pavlovych and Aleks Oniszczak, who helped produce a video profiling TnToolkit. Most importantly, I would like to thank my parents, Betty and Sandro, for their love, encouragement, and support. vi

Technical advancements involving the pervasive cell phone make it a viable computing platform. As such, the input of textual information is vital. Use of ambiguous keypads preserves the device's portable nature by mapping multiple letters to a single key. Metrics exist to assess such keypads, but they do not account for the physical resources employed by keypads, or for the perceptual and cognitive load placed on the user. Furthermore, existing tools typically encapsulate only one metric, making detailed evaluation cumbersome and time-consuming. This thesis outlines a new metric that quantifies a keypad's efficiency using the number of text entry keys it employs. It also presents a revised performance model that incorporates perceptual and cognitive timing values to reflect actual practice. Encompassing both contributions is an original toolkit that simplifies and streamlines analysis of ambiguous keypads. v ACKNOWLEDGEMENTS I would like to convey my sincerest gratitude to my supervisor, Dr. Scott MacKenzie, for his knowledge, guidance, and support. His generous funding of me via his NSERC grant made this research possible. Thanks also go to Dr. Wolfgang Stuerzlinger, who introduced me to the graduate program, Dr. Bil Tzerpos, and Dr. Stephen Chen for taking time out of their busy schedules to serve on my thesis examination committee. In addition, thanks go to Chris Klochek, who served as a preliminary participant for the evaluation described in Chapter 3, and to Andriy Pavlovych and Aleks Oniszczak, who helped produce a video profiling TnToolkit. Most importantly, I would like to thank my parents, Betty and Sandro, for their love, encouragement, and support. vi

The pervasive use of ambiguous keypads for mobile text entry necessitates examination of their performance characteristics. This paper presents TnToolkit-a self-contained tool to calculate performance measurements for ambiguous keypads. While TnToolkit's focus is ambiguous keypads, it also works with QWERTY and on-screen keypads. By default, TnToolkit predicts words-per-minute performance based on a traditional Fitts' law model, and calculates KSPC, the average keystrokes required to enter each character of text. Existing modules are extensible to implement custom metrics. An experiment reveals that using TnToolkit to gather performance metrics is 69% faster than existing techniques, without compromising accuracy.

We introduce new metrics to help explain 3D pointing device movement characteristics. We present a study to assess these by comparing two cursor control modes using a Sony PS Move. "Laser" mode used ray casting, while "position" mode mapped absolute device movement to cursor position. Mouse pointing was also included, and all techniques were also analyzed with existing 2D accuracy measures. Results suggest that position mode shows promise due to its accurate and smooth pointer movements. Our 3D movement metrics do not correlate well with performance, but may be beneficial in understanding how devices are used.

We evaluated and compared four input methods using the Nintendo Wii Remote for pointing tasks. The methods used (i) the "A" button on top of the device, (ii) the "B" button on the bottom of the device, (iii) the Intec Wii Combat Shooter attachment and (iv) the Nintendo Wii Zapper attachment. Fitts' throughput for all four input methods was calculated for both button-up and button-down events. Results indicate that the throughput of the Wii Remote using the A button is 2.85 bps for button-down events. Performance with the Intec Wii Combat Shooter attachment was significantly worse than with the other input methods, likely due to the trigger mechanism. Throughput for button-down target selection using the B button was highest at 2.93 bps.

A touch-based text entry method was developed with the goal of reducing the attention demand on the user. The method, called H4touch, supports the entry of about 75 symbols and commands using only four soft keys. Each key is about 20 larger than the keys on a Qwerty soft keyboard on the same device. Symbols are entered as Huffman codes with most letters requiring just two or three taps. The codes for the letters and common commands are learned after a few hours of practice. At such time, the user can enter text while visually attending to the message, rather than the keyboard. Similar eye-on-the-message interaction is not possible with a Qwerty soft keyboard, since there are many keys and they are small. Entry speeds of 20+ wpm are possible for skilled users.

The popularity of mobile devices with large screens is making single-handed interaction difficult. We propose and evaluate a novel design point around a tilt-based text entry technique which supports single handed usage. Our technique is based on the gesture keyboard (shape writing). However, instead of drawing gestures with a finger or stylus, users articulate a gesture by tilting the device. This can be especially useful when the user's other hand is otherwise encumbered or unavailable. We show that novice users achieve an entry rate of 15 wordsper-minute (wpm) after minimal practice. A pilot longitudinal study reveals that a single participant achieved an entry rate of 32 wpm after approximate 90 minutes of practice. Our data indicate that tilt-based gesture keyboard entry enables walk-up use and provides a suitable text entry rate for occasional use and can act as a promising alternative to single-handed typing in certain situations.

Many mobile devices use touchscreens for text entry. They also provide vibration and audio feedback to simulate interaction with a physical keyboard. To facilitate design decisions of new mobile text entry methods, we conducted a survey and a user study to measure user’s feedback preference and the effect of feedback modes on typing performance. We found that nearly half of respondents prefer no aural or haptic feedback. In our study, feedback mode had no statistically significant effect on entry speed or accuracy. However, comments made by users suggest that feedback preference is due to situational factors, rather than performance considerations.

The H4 technique facilitates text entry with key sequences created using Huffman coding. This study evaluates the use of touch and motion-sensing gestures for H4 input. Touch input yielded better entry speeds (6.6 wpm, versus 5.3 wpm with motion-sensing) and more favourable participant feedback. Accuracy metrics did not differ significantly between the two conditions. Changes to the H4 technique are proposed and the associated benefits and drawbacks are presented.

The author reserves other publication rights, and neither the thesis nor extensive extracts from it may be printed or otherwise reproduced without the author's written permission. Permission has been granted to: a) YORK UNIVERSITY LIBRARIES to lend or sell copies of this thesis in paper, microform or electronic formats, and b) LIBRARY AND ARCHIVES CANADA to reproduce, lend, distribute, or sell copies of this thesis anywhere in the world in microform, paper or electronic formats and to authorise or procure the reproduction, loan, distribution or sale of copies of this thesis anywhere in the world in microform, paper or electronic formats. By virtue of submitting this document electronically, the author certifies that this is a true electronic equivalent of the copy of the thesis approved by York University for the award of the degree. No alteration of the content has occurred and if there are any minor variations in formatting, they are as a result of the conversion to Adobe Acro...

Computer users commonly use multi-step text entry methods on handheld devices and as alternative input methods for accessibility. These methods are also commonly used to enter text in languages with large alphabets or complex writing systems. These methods require performing multiple actions simultaneously (chorded) or in a specific sequence (constructive) to produce input. However, evaluating these methods is difficult since traditional performance metrics were designed explicitly for non-ambiguous, uni-step methods (e.g., QWERTY). They fail to capture the actual performance of a multi-step method and do not provide enough detail to aid in design improvements. We present three new action-level performance metrics: UnitER, UA, and UnitCX. They account for the error rate, accuracy, and complexity of multi-step chorded and constructive methods. They describe the multiple inputs that comprise typing a single character—action-level metrics observe actions performed to type one character...

Fifteen participants completed a study comparing video game controllers for point-select tasks. We used a Fitts ’ law task, as per ISO 9241-9, using the Nintendo Wii Remote for infrared pointing, the Nintendo Classic Controller for analogue stick pointing, and a standard mouse as a baseline condition. The mouse had the highest throughput at 3.78 bps. Both game controllers performed poorly by comparison. The Wii Remote throughput was 31.5% lower, at 2.59 bps, and the Classic Controller 60.8 % lower at 1.48 bps. Comparing just the video game controllers, the Wii Remote presents a 75 % increase in throughput over the Classic Controller. Error rates for the mouse, Classic Controller, and the Wii Remote were 3.53%, 6.58%, and 10.2%, respectively. Fourteen of 15 participants expressed a preference for the Wii Remote over the Classic Controller for pointing tasks in a home entertainment environment.

This paper introduces UniGest, a technique that provides pointer input and text entry in a single device without occupying the display. It uses a Nintendo Wii motion-sensing remote to capture gestures that are mapped to character input. A gesture alphabet is proposed, with each gesture composed of at most two primitive motions. A web-based user study measured movement times for primitive motions. Results range from 296 to 481 ms, implying an upper-bound UniGest text entry performance prediction of 27.9 wpm. ACM Classification Keywords H5.2 [Information interfaces and presentation]: User Interfaces – Input devices and strategies.

The pervasive use of ambiguous keypads for mobile text entry necessitates examination of their performance characteristics. This paper presents TnToolkit – a self-contained tool to calculate performance measurements for ambiguous keypads. While TnToolkit’s focus is ambiguous keypads, it also works with QWERTY and on-screen keypads. By default, TnToolkit predicts words-per-minute performance based on a traditional Fitts ’ law model, and calculates KSPC, the average keystrokes required to enter each character of text. Existing modules are extensible to implement custom metrics. An experiment reveals that using TnToolkit to gather performance metrics is 69 % faster than existing techniques, without compromising accuracy. here provides a fast and accurate analysis of ambiguous keypad characteristics, including text entry performance in words per minute (WPM) and keystrokes-per-character (KSPC). The ubiquitous telephone keypad is an example of an ambiguous keypad. Even the popular Ninten...

Abstract-The H4 technique facilitates text entry with key sequences created using Huffman coding. This study evaluates the use of touch and motion-sensing gestures for H4 input. Touch input yielded better entry speeds (6.6 wpm, versus 5.3 wpm with motion-sensing) and more favourable participant feedback. Accuracy metrics did not differ significantly between the two conditions. Changes to the H4 technique are proposed and the associated benefits and drawbacks are presented.

The author reserves other publication rights, and neither the thesis nor extensive extracts from it may be printed or otherwise reproduced without the author’s written permission. Permission has been granted to: a) YORK UNIVERSITY LIBRARIES to lend or sell copies of this thesis in paper, microform or electronic formats, and b) LIBRARY AND ARCHIVES CANADA to reproduce, lend, distribute, or sell copies of this thesis anywhere in the world in microform, paper or electronic formats and to authorise or procure the reproduction, loan, distribution or sale of copies of this thesis anywhere in the world in microform, paper or electronic formats.

Abstract- Many mobile devices use touchscreens for text entry. They also provide vibration and audio feedback to simulate interaction with a physical keyboard. To facilitate design decisions of new mobile text entry methods, we conducted a survey and a user study to measure user’s feedback preference and the effect of feedback modes on typing performance. We found that nearly half of respondents prefer no aural or haptic feedback. In our study, feedback mode had no statistically significant effect on entry speed or accuracy. However, comments made by users suggest that feedback preference is due to situational factors, rather than performance considerations.

Abstract-We developed TEMA, an application to gather Text Entry speed and accuracy Metrics on Android devices. This paper details the features of the application, describes a user study to demonstrate its utility, and establishes entry speed and accuracy measurements for the evaluated text entry techniques. We evaluated and compared four mobile text entry methods: two-thumb QWERTY typing, one-finger QWERTY typing, handwriting recognition, and shape writing recognition. The two QWERTY techniques were the fastest, with no statistically significant difference between them in entry speed or accuracy. Shape writing was slightly slower, but similar in accuracy. Handwriting was the slowest and least accurate technique.

Abstract- Many mobile devices use touchscreens for text entry. They also provide vibration and audio feedback to simulate interaction with a physical keyboard. To facilitate design decisions of new mobile text entry methods, we conducted a survey and a user study to measure user’s feedback preference and the effect of feedback modes on typing performance. We found that nearly half of respondents prefer no aural or haptic feedback. In our study, feedback mode had no statistically significant effect on entry speed or accuracy. However, comments made by users suggest that feedback preference is due to situational factors, rather than performance considerations.

Abstract-We developed TEMA, an application to gather Text Entry speed and accuracy Metrics on Android devices. This paper details the features of the application, describes a user study to demonstrate its utility, and establishes entry speed and accuracy measurements for the evaluated text entry techniques. We evaluated and compared four mobile text entry methods: two-thumb QWERTY typing, one-finger QWERTY typing, handwriting recognition, and shape writing recognition. The two QWERTY techniques were the fastest, with no statistically significant difference between them in entry speed or accuracy. Shape writing was slightly slower, but similar in accuracy. Handwriting was the slowest and least accurate technique.