Model-Based Design of Scanning Input Communication Aids: State of the Art and Research Issues

Abstract Scanning input communication aids are used by people with speech and motor disabilities. These aids are typically in the form of on-screen matrix of alphanumeric characters. To compose communicative messages, disabled users select characters from the matrix with scanning input methods. The problem faced by designers of such systems is to choose the best design from a large number of design alternatives. The problem may be alleviated by automated design methods based on user models. We performed a critical analysis of the related work and found that the present user models do not consider several factors that are important for user-system interaction. These include (a) scanning interaction, (b) visual search, and (c) user errors. In this paper, we present review and analysis of existing models and suggest directions for future research.

[1]  John L. Arnott Text Entry in Augmentative and Alternative Communication , 2005, Efficient Text Entry.

[2]  R. William Soukoreff,et al.  Text entry for mobile computing: models and methods , 2002 .

[3]  Shumin Zhai,et al.  Performance Optimization of Virtual Keyboards , 2002, Hum. Comput. Interact..

[4]  I.,et al.  Fitts' Law as a Research and Design Tool in Human-Computer Interaction , 1992, Hum. Comput. Interact..

[5]  David J. Ward,et al.  Fast Hands-free Writing by Gaze Direction , 2002, ArXiv.

[6]  Allen Newell,et al.  The keystroke-level model for user performance time with interactive systems , 1980, CACM.

[7]  G W Lesher,et al.  Optimal character arrangements for ambiguous keyboards. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[8]  I. Scott MacKenzie,et al.  Theoretical upper and lower bounds on typing speed using a stylus and a soft keyboard , 1995, Behav. Inf. Technol..

[9]  J. Shaoul Human Error , 1973, Nature.

[10]  Howell Istance,et al.  Providing motor impaired users with access to standard Graphical User Interface (GUI) software via eye-based interaction , 1996 .

[11]  C. Shewan,et al.  Augmentative and Alternative Communication , 2020, Encyclopedia of Education and Information Technologies.

[12]  Andrew Sears,et al.  The role of visual search in the design of effective soft keyboards , 2001, Behav. Inf. Technol..

[13]  Simon P. Levine,et al.  Modeling the speed of text entry with a word prediction interface , 1994 .

[14]  Ann Copestake Augmented and alternative NLP techniques for augmentative and alternative communication , 1997, Workshop On Natural Language Processing For Communication Aids.

[15]  Karin Harbusch,et al.  An Evaluation Study of Two–Button Scanning with Ambiguous Keyboards , 2004 .

[16]  W. E. Hick Quarterly Journal of Experimental Psychology , 1948, Nature.

[17]  R. Damper Text composition by the physically disabled: a rate prediction model for scanning input. , 1984, Applied ergonomics.

[18]  David J. Ward,et al.  Artificial intelligence: Fast hands-free writing by gaze direction , 2002, Nature.

[19]  Horabail S. Venkatagiri Efficient keyboard layouts for sequential access in augmentative and alternative communication , 1999 .

[20]  David E. Kieras,et al.  Using GOMS for user interface design and evaluation: which technique? , 1996, TCHI.

[21]  John Todman,et al.  Rate and quality of conversations using a text-storage AAC system: Single-case training study , 2000 .

[22]  I. Scott MacKenzie,et al.  Text Entry for Mobile Computing: Models and Methods,Theory and Practice , 2002, Hum. Comput. Interact..

[23]  I. Scott MacKenzie,et al.  The design and evaluation of a high-performance soft keyboard , 1999, CHI '99.

[24]  Wayne D. Gray The nature and processing of errors in interactive behavior , 2000, Cogn. Sci..

[25]  David E. Kieras,et al.  The GOMS family of user interface analysis techniques: comparison and contrast , 1996, TCHI.

[26]  Brad A. Myers,et al.  Trackball text entry for people with motor impairments , 2006, CHI.

[27]  Shumin Zhai,et al.  Physics-based graphical keyboard design , 2000, CHI Extended Abstracts.

[28]  P. Fitts,et al.  INFORMATION CAPACITY OF DISCRETE MOTOR RESPONSES. , 1964, Journal of experimental psychology.

[29]  Philip Constantinou,et al.  Designing human-computer interfaces for quadriplegic people , 2003, TCHI.

[30]  I. Scott MacKenzie,et al.  Text entry using soft keyboards , 1999, Behav. Inf. Technol..

[31]  John Paulin Hansen,et al.  Gaze typing compared with input by head and hand , 2004, ETRA.

[32]  John Paulin Hansen,et al.  Augmentative and Alternative Communication: The Future of Text on the Move , 2002, User Interfaces for All.

[33]  Albert M. Cook,et al.  Assistive Technologies: Principles and Practice , 1995 .

[34]  Simon P. Levine,et al.  Model simulations of user performance with word prediction , 1998 .

[35]  Mathieu Raynal,et al.  Genetic algorithm to generate optimized soft keyboard , 2005, CHI Extended Abstracts.

[36]  Afsaneh Fazly The Use of Syntax in Word Completion Utilities , 2002 .

[37]  P. Fitts The information capacity of the human motor system in controlling the amplitude of movement. , 1954, Journal of experimental psychology.

[38]  Gregory W. Lesher,et al.  Techniques for augmenting scanning communication , 1998 .

[39]  Allen Newell,et al.  The psychology of human-computer interaction , 1983 .

[40]  John L. Arnott,et al.  A script-based AAC system for transactional interaction , 1998, Natural Language Engineering.

[41]  Andrew Sears,et al.  Layout Appropriateness: A Metric for Evaluating User Interface Widget Layout , 1993, IEEE Trans. Software Eng..

[42]  Simon P. Levine,et al.  Modeling of user performance with computer access and augmentative communication systems for handicapped people , 1990 .

[43]  R. Hyman Stimulus information as a determinant of reaction time. , 1953, Journal of experimental psychology.

[44]  C Goodenough-Trepagnier,et al.  Customised text entry devices for motor-impaired users. , 1990, Applied ergonomics.

[45]  Shumin Zhai,et al.  The metropolis keyboard - an exploration of quantitative techniques for virtual keyboard design , 2000, UIST '00.

[46]  Simon P. Levine,et al.  Keystroke-Level Models for User Performance with Word Prediction , 1997 .

[47]  Kathleen F. McCoy,et al.  Compansion: From research prototype to practical integration , 1998, Natural Language Engineering.

[48]  Peter Robinson,et al.  Investigating the applicability of user models for motion-impaired users , 2000, Assets '00.

[49]  Shari Trewin,et al.  Keyboard and mouse errors due to motor disabilities , 1999, Int. J. Hum. Comput. Stud..

[50]  Kathleen F. McCoy,et al.  Generating text from compressed input: an intelligent interface for people with severe motor impairments , 1992, CACM.