Text Entry in the E-Commerce Age: Two Proposals for the Severely Handicapped

People with severe motor disabilities have extreme access difficulties with all kinds of web services especially when they want not only to surf the web, but also write some text, e.g., to participate in an e-activity. Several problems arise when using traditional scanning systems, such as the low text entry rate, the time consuming task of learning the scan matrix layout, or simply, the poor visibility of the web page due to the large surface needed to display the complete scan matrix on the screen. We propose a reduced virtual keyboard based on scanning with only one switch as input device. The scan matrix consists of only three cells, so ambiguity is present due to the assignment of 26 characters to the three keys. Word-level and character-level disambiguation modes are explored using a mathematical model, and the text entry rates for an expert user were 15.9 and 10.3 words per minute respectively, using a scan period of 0.5 seconds. This keyboard could be embedded into a web page using a Java applet, JavaScript code or a Flash application, or be programmed as an independent application.

[1]  I. Scott MacKenzie,et al.  KSPC (Keystrokes per Character) as a Characteristic of Text Entry Techniques , 2002, Mobile HCI.

[2]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[3]  Mark D. Dunlop,et al.  Predictive text entry methods for mobile phones , 2000, Personal Technologies.

[4]  Ian H. Witten,et al.  The Reactive Keyboard , 1992 .

[5]  Anind K. Dey,et al.  Web accessibility for low bandwidth input , 2002, ASSETS.

[6]  Michael Kühn,et al.  Predictive and highly ambiguous typing for a severely speech and motion impaired user , 2001, HCI.

[7]  Guy Bourhis,et al.  Interaction between a Disabled Person and a Scanning Communication Aid: Towards an Automatic Adjustment of the Scanning Rate Adapted to the User , 2008, ICCHP.

[8]  Constantine Stephanidis,et al.  Universal access in the information society , 1999, HCI.

[9]  Kumiko Tanaka-Ishii,et al.  Text Entry Using a Small Number of Buttons , 2007 .

[10]  Kumiko Tanaka-Ishii.  Word-based predictive text entry using adaptive language models , 2007, Nat. Lang. Eng..

[11]  Kumiko Tanaka-Ishii,et al.  Entering Text with a Four-Button Device , 2002, COLING.

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

[13]  Gregory W. Lesher,et al.  TECHNIQUES FOR AUTOMATICALLY UPDATING SCANNING DELAYS , 2000 .

[14]  Jan H. P. Eloff,et al.  Accessible Computer Interaction for People with Disabilities: The Case of Quadriplegics , 2004, ICEIS.

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

[16]  Karin Harbusch,et al.  Towards an adaptive communication aid with text input from ambiguous keyboards , 2003 .

[17]  R. Metts,et al.  DISABILITY ISSUES, TRENDS AND RECOMMENDATIONS FOR THE WORLD BANK (FULL TEXT AND ANNEXES) , 2000 .

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

[19]  Nestor Garay-Vitoria,et al.  Text prediction systems: a survey , 2006, Universal Access in the Information Society.

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

[21]  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.

[22]  Julio Miró,et al.  Text Entry System Based on a Minimal Scan Matrix for Severely Physically Handicapped People , 2008, ICCHP.

[23]  Gregg C. Vanderheiden,et al.  Web content accessibility guidelines 1.0 , 2001, INTR.

[24]  I. Scott MacKenzie,et al.  LetterWise: prefix-based disambiguation for mobile text input , 2001, UIST '01.

[25]  Richard C. Simpson,et al.  Evaluation of an adaptive row/column scanning system , 2006 .

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

[27]  Christina L. James,et al.  Text input for mobile devices: comparing model prediction to actual performance , 2001, CHI.

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

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

[30]  John L. Arnott,et al.  Probabilistic character disambiguation for reduced keyboards using small text samples , 1992 .

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

[32]  J. Abascal,et al.  Opportunities and risks of the information and communication technologies for users with special needs , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[33]  P. Fitts.  The information capacity of the human motor system in controlling the amplitude of movement. 1954. , 1992, Journal of experimental psychology. General.

[34]  P Demasco.  Human factors considerations in the design of language interfaces in AAC. , 1994, Assistive technology : the official journal of RESNA.

[35]  H H Koester,et al.  Adaptive one-switch row-column scanning. , 1999, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[36]  Kevin Curran,et al.  Investigating text input methods for mobile phones , 2006, Telematics Informatics.

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

[38]  H H Koester,et al.  Learning and performance of able-bodied individuals using scanning systems with and without word prediction. , 1994, Assistive technology : the official journal of RESNA.