The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform

David J C Mackay - One of the best experts on this subject based on the ideXlab platform.

  • ticker an adaptive single switch Text Entry Method for visually impaired users
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
    Co-Authors: Per Ola Kristensson, David J C Mackay
    Abstract:

    Ticker is a probabilistic stereophonic single-switch Text Entry Method for visually-impaired users with motor disabilities who rely on single-switch scanning systems to communicate. Such scanning systems are sensitive to a variety of noise sources, which are inevitably introduced in practical use of single-switch systems. Ticker uses a novel interaction model based on stereophonic sound coupled with statistical models for robust inference of the user's intended Text in the presence of noise. As a consequence of its design, Ticker is resilient to noise and therefore a practical solution for single-switch scanning systems. Ticker's performance is validated using a combination of simulations and empirical user studies.

  • the statistical model for ticker an adaptive single switch Text Entry Method for visually impaired users
    arXiv: Artificial Intelligence, 2018
    Co-Authors: Per Ola Kristensson, David J C Mackay
    Abstract:

    This paper presents the statistical model for Ticker [1], a novel probabilistic stereophonic single-switch Text Entry Method for visually-impaired users with motor disabilities who rely on single-switch scanning systems to communicate. All terminology and notation are defined in [1].

Peter Tarasewich - One of the best experts on this subject based on the ideXlab platform.

  • improving dictionary based disambiguation Text Entry Method accuracy
    Human Factors in Computing Systems, 2007
    Co-Authors: Jun Gong, Peter Tarasewich, Carole D Hafner, Scott Mackenzie
    Abstract:

    Text Entry on mobile devices is problematic because of ever-decreasing device sizes. Dictionary-based keypad Text Entry Methods are relatively effective, but still run into problems of word ambiguity, especially when used with small numbers of keys. Common Text Entry disambiguation Methods only use word frequency information to resolve conflicts. This paper proposes a new Method that also looks at semantic information (distances between word meanings). Simulations show encouraging results, suggesting potential practical applications of this Method to mobile devices.

  • CHI Extended Abstracts - Improving dictionary-based disambiguation Text Entry Method accuracy
    CHI '07 extended abstracts on Human factors in computing systems - CHI '07, 2007
    Co-Authors: Jun Gong, Peter Tarasewich, Carole D Hafner, Scott Mackenzie
    Abstract:

    Text Entry on mobile devices is problematic because of ever-decreasing device sizes. Dictionary-based keypad Text Entry Methods are relatively effective, but still run into problems of word ambiguity, especially when used with small numbers of keys. Common Text Entry disambiguation Methods only use word frequency information to resolve conflicts. This paper proposes a new Method that also looks at semantic information (distances between word meanings). Simulations show encouraging results, suggesting potential practical applications of this Method to mobile devices.

  • Improved Text Entry for mobile devices: alternate keypad designs and novel predictive disambiguation Methods
    2007
    Co-Authors: Peter Tarasewich, Jun Gong
    Abstract:

    Despite the ever-increasing popularity of mobile devices, Text Entry on such devices is becoming more of a challenge. Problems primarily lie with shrinking device sizes, which can greatly limit available display space, as well as require unique input modalities and interaction techniques. In attempting to resolve this issue, researchers have found that dictionary-based predictive disambiguation Text Entry Methods are fairly efficient for Text Entry on devices such as mobile phones that use keypads instead of full keyboards. This type of Text Entry Method "guesses" the word that a user desires by matching their sequence of keystrokes against feasible entries saved in a dictionary. However, word ambiguity, limited dictionary sizes, and large learning curves still prevent this Method from being more widely adopted in many situations, and on more mobile devices. Innovative solutions to these problems, focusing on both physical keypad designs and predictive disambiguation Methods, are introduced in this dissertation work. The first part of this dissertation describes a set of keypad designs which are optimized under the constraint of keeping characters in alphabetical order across keys. Designs were found that have performance close to that of unconstrained designs, while maintaining better novice usability. The second part proposes a novel predictive disambiguation Method which utilizes not only word frequency information, as do most existing dictionary-based predictive disambiguation Methods, but also semantic and syntactical Text information to help disambiguate the user's desired words. Simulations and an empirical user study have shown improvements in Text Entry speed of up to 9.6% and reductions in the number of user errors of up to 21.2%. Furthermore, this dissertation presents a new error metric that is capable of revealing more information about user performance during experiments involving Text Entry Methods. In summary, this dissertation work focused on creating and validating improved Methods for Text Entry on mobile devices.

  • a new error metric for Text Entry Method evaluation
    Human Factors in Computing Systems, 2006
    Co-Authors: Jun Gong, Peter Tarasewich
    Abstract:

    On devices such as mobile phones, Text is often entered using keypads and predictive Text Entry techniques. Current metrics used for measuring Text Entry error rates have limitations in terms of the types of errors they account for, and cannot easily distinguish between different types of errors. This research proposes a new Text Entry error metric that addresses some of the outstanding issues that exist with current metrics. Specifically, the metric accounts in detail for the way the user handles corrections during Text Entry, moving beyond current keystroke level error measurement. The feasibility and usefulness of this new metric is shown through the analysis of an experiment that tests an alphabetically constrained keypad design that includes upper and lower case letters, numbers, and punctuation marks.

  • CHI - A new error metric for Text Entry Method evaluation
    Proceedings of the SIGCHI conference on Human Factors in computing systems - CHI '06, 2006
    Co-Authors: Jun Gong, Peter Tarasewich
    Abstract:

    On devices such as mobile phones, Text is often entered using keypads and predictive Text Entry techniques. Current metrics used for measuring Text Entry error rates have limitations in terms of the types of errors they account for, and cannot easily distinguish between different types of errors. This research proposes a new Text Entry error metric that addresses some of the outstanding issues that exist with current metrics. Specifically, the metric accounts in detail for the way the user handles corrections during Text Entry, moving beyond current keystroke level error measurement. The feasibility and usefulness of this new metric is shown through the analysis of an experiment that tests an alphabetically constrained keypad design that includes upper and lower case letters, numbers, and punctuation marks.

Jun Gong - One of the best experts on this subject based on the ideXlab platform.

  • improving dictionary based disambiguation Text Entry Method accuracy
    Human Factors in Computing Systems, 2007
    Co-Authors: Jun Gong, Peter Tarasewich, Carole D Hafner, Scott Mackenzie
    Abstract:

    Text Entry on mobile devices is problematic because of ever-decreasing device sizes. Dictionary-based keypad Text Entry Methods are relatively effective, but still run into problems of word ambiguity, especially when used with small numbers of keys. Common Text Entry disambiguation Methods only use word frequency information to resolve conflicts. This paper proposes a new Method that also looks at semantic information (distances between word meanings). Simulations show encouraging results, suggesting potential practical applications of this Method to mobile devices.

  • CHI Extended Abstracts - Improving dictionary-based disambiguation Text Entry Method accuracy
    CHI '07 extended abstracts on Human factors in computing systems - CHI '07, 2007
    Co-Authors: Jun Gong, Peter Tarasewich, Carole D Hafner, Scott Mackenzie
    Abstract:

    Text Entry on mobile devices is problematic because of ever-decreasing device sizes. Dictionary-based keypad Text Entry Methods are relatively effective, but still run into problems of word ambiguity, especially when used with small numbers of keys. Common Text Entry disambiguation Methods only use word frequency information to resolve conflicts. This paper proposes a new Method that also looks at semantic information (distances between word meanings). Simulations show encouraging results, suggesting potential practical applications of this Method to mobile devices.

  • Improved Text Entry for mobile devices: alternate keypad designs and novel predictive disambiguation Methods
    2007
    Co-Authors: Peter Tarasewich, Jun Gong
    Abstract:

    Despite the ever-increasing popularity of mobile devices, Text Entry on such devices is becoming more of a challenge. Problems primarily lie with shrinking device sizes, which can greatly limit available display space, as well as require unique input modalities and interaction techniques. In attempting to resolve this issue, researchers have found that dictionary-based predictive disambiguation Text Entry Methods are fairly efficient for Text Entry on devices such as mobile phones that use keypads instead of full keyboards. This type of Text Entry Method "guesses" the word that a user desires by matching their sequence of keystrokes against feasible entries saved in a dictionary. However, word ambiguity, limited dictionary sizes, and large learning curves still prevent this Method from being more widely adopted in many situations, and on more mobile devices. Innovative solutions to these problems, focusing on both physical keypad designs and predictive disambiguation Methods, are introduced in this dissertation work. The first part of this dissertation describes a set of keypad designs which are optimized under the constraint of keeping characters in alphabetical order across keys. Designs were found that have performance close to that of unconstrained designs, while maintaining better novice usability. The second part proposes a novel predictive disambiguation Method which utilizes not only word frequency information, as do most existing dictionary-based predictive disambiguation Methods, but also semantic and syntactical Text information to help disambiguate the user's desired words. Simulations and an empirical user study have shown improvements in Text Entry speed of up to 9.6% and reductions in the number of user errors of up to 21.2%. Furthermore, this dissertation presents a new error metric that is capable of revealing more information about user performance during experiments involving Text Entry Methods. In summary, this dissertation work focused on creating and validating improved Methods for Text Entry on mobile devices.

  • a new error metric for Text Entry Method evaluation
    Human Factors in Computing Systems, 2006
    Co-Authors: Jun Gong, Peter Tarasewich
    Abstract:

    On devices such as mobile phones, Text is often entered using keypads and predictive Text Entry techniques. Current metrics used for measuring Text Entry error rates have limitations in terms of the types of errors they account for, and cannot easily distinguish between different types of errors. This research proposes a new Text Entry error metric that addresses some of the outstanding issues that exist with current metrics. Specifically, the metric accounts in detail for the way the user handles corrections during Text Entry, moving beyond current keystroke level error measurement. The feasibility and usefulness of this new metric is shown through the analysis of an experiment that tests an alphabetically constrained keypad design that includes upper and lower case letters, numbers, and punctuation marks.

  • CHI - A new error metric for Text Entry Method evaluation
    Proceedings of the SIGCHI conference on Human Factors in computing systems - CHI '06, 2006
    Co-Authors: Jun Gong, Peter Tarasewich
    Abstract:

    On devices such as mobile phones, Text is often entered using keypads and predictive Text Entry techniques. Current metrics used for measuring Text Entry error rates have limitations in terms of the types of errors they account for, and cannot easily distinguish between different types of errors. This research proposes a new Text Entry error metric that addresses some of the outstanding issues that exist with current metrics. Specifically, the metric accounts in detail for the way the user handles corrections during Text Entry, moving beyond current keystroke level error measurement. The feasibility and usefulness of this new metric is shown through the analysis of an experiment that tests an alphabetically constrained keypad design that includes upper and lower case letters, numbers, and punctuation marks.

Per Ola Kristensson - One of the best experts on this subject based on the ideXlab platform.

  • ticker an adaptive single switch Text Entry Method for visually impaired users
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
    Co-Authors: Per Ola Kristensson, David J C Mackay
    Abstract:

    Ticker is a probabilistic stereophonic single-switch Text Entry Method for visually-impaired users with motor disabilities who rely on single-switch scanning systems to communicate. Such scanning systems are sensitive to a variety of noise sources, which are inevitably introduced in practical use of single-switch systems. Ticker uses a novel interaction model based on stereophonic sound coupled with statistical models for robust inference of the user's intended Text in the presence of noise. As a consequence of its design, Ticker is resilient to noise and therefore a practical solution for single-switch scanning systems. Ticker's performance is validated using a combination of simulations and empirical user studies.

  • the statistical model for ticker an adaptive single switch Text Entry Method for visually impaired users
    arXiv: Artificial Intelligence, 2018
    Co-Authors: Per Ola Kristensson, David J C Mackay
    Abstract:

    This paper presents the statistical model for Ticker [1], a novel probabilistic stereophonic single-switch Text Entry Method for visually-impaired users with motor disabilities who rely on single-switch scanning systems to communicate. All terminology and notation are defined in [1].

  • Mobile HCI - The inviscid Text Entry rate and its application as a grand goal for mobile Text Entry
    Proceedings of the 16th international conference on Human-computer interaction with mobile devices & services - MobileHCI '14, 2014
    Co-Authors: Per Ola Kristensson, Keith Vertanen
    Abstract:

    We introduce the concept of the inviscid Text Entry rate: the point when the user's creativity is the bottleneck rather than the Text Entry Method. We then apply the inviscid Text Entry rate to define a grand goal for mobile Text Entry. Via a proxy measure we estimate the population mean of the sufficiently inviscid Entry rate to be 67 wpm. We then compare existing mobile Text Entry Methods against this estimate and find that the vast majority of Text Entry Methods in the literature are substantially slower. This analysis suggests the mobile Text Entry field needs to focus on Methods that can viably approach the inviscid Entry rate.

  • Complementing Text Entry evaluations with a composition task
    ACM Transactions on Computer-Human Interaction, 2014
    Co-Authors: Keith Vertanen, Per Ola Kristensson
    Abstract:

    A common Methodology for evaluating Text Entry Methods is to ask participants to transcribe a predefined set of memorable sentences or phrases. In this article, we explore if we can complement the conventional transcription task with a more externally valid composition task. In a series of large-scale crowdsourced experiments, we found that participants could consistently and rapidly invent high quality and creative compositions with only modest reductions in Entry rates. Based on our series of experiments, we provide a best-practice procedure for using composition tasks in Text Entry evaluations. This includes a judging protocol which can be performed either by the experimenters or by crowdsourced workers on a microtask market. We evaluated our composition task procedure using a Text Entry Method unfamiliar to participants. Our empirical results show that the composition task can serve as a valid complementary Text Entry evaluation Method.

  • CHI Extended Abstracts - Learning shape writing by game playing
    CHI '07 extended abstracts on Human factors in computing systems - CHI '07, 2007
    Co-Authors: Per Ola Kristensson, Shumin Zhai
    Abstract:

    We present a computer game designed to efficiently and playfully teach users shape writing - a new Text Entry Method for pen-based devices.

Huijun Xu - One of the best experts on this subject based on the ideXlab platform.

  • Edutainment - Multi-stroke freehand Text Entry Method using OpenVG and its application on mobile devices
    Technologies for E-Learning and Digital Entertainment, 2006
    Co-Authors: Gaoqi He, Christophe Quarre, Mingmin Zhang, Huijun Xu
    Abstract:

    A new freehand Text Entry Method, called EasyStroke, is presented for mobile devices. Based on the multi-stroke philosophy and analogy principle, 26 lowercase characters are described. Trails of stylus movement are segmented dynamically and best approximated through the three captured points. Then, each segment is rendered immediately using hardware-accelerated OpenVG APIs. Finally, combination of the types of all segments flows into the small LUT-based recognizer, using some additional bits. Thus, this new Method is easy, efficient and quick to use. Implementation and evaluation of such system are presented. Prototype of mobile game, WordCollection, is demonstrated to help users experience functions of both education and entertainment.

  • multi stroke freehand Text Entry Method using openvg and its application on mobile devices
    Lecture Notes in Computer Science, 2006
    Co-Authors: Gaoqi He, Christophe Quarre, Mingmin Zhang, Huijun Xu
    Abstract:

    A new freehand Text Entry Method, called EasyStroke, is presented for mobile devices. Based on the multi-stroke philosophy and analogy principle, 26 lowercase characters are described. Trails of stylus movement are segmented dynamically and best approximated through the three captured points. Then, each segment is rendered immediately using hardware-accelerated OpenVG APIs. Finally, combination of the types of all segments flows into the small LUT-based recognizer, using some additional bits. Thus, this new Method is easy, efficient and quick to use. Implementation and evaluation of such system are presented. Prototype of mobile game, WordCollection, is demonstrated to help users experience functions of both education and entertainment.