Typing Performance of Blind Users: An Analysis of Touch Behaviors, Learning Effect, and In-Situ Usage

Non-visual text-entry for people with visual impairments has focused mostly on the comparison of input techniques reporting on performance measures, such as accuracy and speed. While researchers have been able to establish that non-visual input is slow and error prone, there is little understanding on how to improve it. To develop a richer characterization of typing performance, we conducted a longitudinal study with five novice blind users. For eight weeks, we collected in-situ usage data and conducted weekly laboratory assessment sessions. This paper presents a thorough analysis of typing performance that goes beyond traditional aggregated measures of text-entry and reports on character-level errors and touch measures. Our findings show that users improve over time, even though it is at a slow rate (0.3 WPM per week). Substitutions are the most common type of error and have a significant impact on entry rates. In addition to text input data, we analyzed touch behaviors, looking at touch contact points, exploration movements, and lift positions. We provide insights on why and how performance improvements and errors occur. Finally, we derive some implications that should inform the design of future virtual keyboards for non-visual input.

[1]  Susumu Harada,et al.  The angle mouse: target-agnostic dynamic gain adjustment based on angular deviation , 2009, CHI.

[2]  Scott P. Robertson,et al.  Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , 1991 .

[3]  Gregory D. Abowd,et al.  An evaluation of BrailleTouch: mobile touchscreen text entry for the visually impaired , 2012, Mobile HCI.

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

[5]  D A Berry,et al.  Logarithmic transformations in ANOVA. , 1987, Biometrics.

[6]  Jacob O. Wobbrock,et al.  Taming wild behavior: the input observer for obtaining text entry and mouse pointing measures from everyday computer use , 2012, CHI.

[7]  Richard E. Ladner,et al.  Input finger detection for nonvisual touch screen text entry in Perkinput , 2012, Graphics Interface.

[8]  Patrick Langdon,et al.  Mouse movements of motion-impaired users: a submovement analysis , 2003, ASSETS.

[9]  I. Scott MacKenzie,et al.  A character-level error analysis technique for evaluating text entry methods , 2002, NordiCHI '02.

[10]  Jacob O. Wobbrock,et al.  Personalized input: improving ten-finger touchscreen typing through automatic adaptation , 2012, CHI.

[11]  Simeon Keates,et al.  Effect of age and Parkinson's disease on cursor positioning using a mouse , 2005, Assets '05.

[12]  Daniel J. Wigdor,et al.  Typing on flat glass: examining ten-finger expert typing patterns on touch surfaces , 2011, CHI.

[13]  Joaquim A. Jorge,et al.  Touch typing using thumbs: understanding the effect of mobility and hand posture , 2012, CHI.

[14]  Joaquim A. Jorge,et al.  Blind people and mobile touch-based text-entry: acknowledging the need for different flavors , 2011, ASSETS.

[15]  Ana Ivelisse Avilés,et al.  Linear Mixed Models for Longitudinal Data , 2001, Technometrics.

[16]  Kyle Montague,et al.  TabLETS Get Physical: Non-Visual Text Entry on Tablet Devices , 2015, CHI.

[17]  I. Scott MacKenzie,et al.  Accuracy measures for evaluating computer pointing devices , 2001, CHI.

[18]  Per Ola Kristensson,et al.  Five Challenges for Intelligent Text Entry Methods , 2009, AI Mag..

[19]  I. Scott MacKenzie,et al.  Metrics for text entry research: an evaluation of MSD and KSPC, and a new unified error metric , 2003, CHI '03.

[20]  Brad A. Myers,et al.  Analyzing the input stream for character- level errors in unconstrained text entry evaluations , 2006, TCHI.

[21]  Grigori E. Evreinov,et al.  Adaptive blind interaction technique for touchscreens , 2006, Universal Access in the Information Society.

[22]  Joaquim A. Jorge,et al.  From Tapping to Touching: Making Touch Screens Accessible to Blind Users , 2008, IEEE MultiMedia.

[23]  C. McCulloch,et al.  Generalized Linear Mixed Models , 2005 .

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

[25]  Jacob O. Wobbrock,et al.  Mouse pointing endpoint prediction using kinematic template matching , 2014, CHI.

[26]  Kumiko Tanaka-Ishii,et al.  Text Entry Systems: Mobility, Accessibility, Universality , 2007 .

[27]  Gregory D. Abowd,et al.  No-Look Notes: Accessible Eyes-Free Multi-touch Text Entry , 2010, Pervasive.

[28]  Joaquim A. Jorge,et al.  Elderly text-entry performance on touchscreens , 2012, ASSETS '12.

[29]  Kyle Montague,et al.  B#: chord-based correction for multitouch braille input , 2014, CHI.

[30]  I. Scott MacKenzie,et al.  Eyes-free text entry with error correction on touchscreen mobile devices , 2010, NordiCHI.