A comparison of area pointing and goal crossing for people with and without motor impairments

Prior work has highlighted the challenges faced by people with motor impairments when trying to acquire on-screen targets using a mouse or trackball. Two reasons for this are the difficulty of positioning the mouse cursor within a confined area, and the challenge of accurately executing a click. We hypothesize that both of these difficulties with area pointing may be alleviated in a different target acquisition paradigm called "goal crossing." In goal crossing, users do not acquire a confined area, but instead pass over a target line. Although goal crossing has been studied for able-bodied users, its suitability for people with motor impairments is unknown. We present a study of 16 people, 8 of whom had motor impairments, using mice and trackballs to do area pointing and goal crossing. Our results indicate that Fitts' law models both techniques for both user groups. Furthermore, although throughput for able-bodied users was higher for area pointing than for goal crossing (4.72 vs. 3.61 bits/s), the opposite was true for users with motor impairments (2.34 vs. 2.88 bits/s), suggesting that goal crossing may be viable for them. However, error rates were higher for goal crossing than for area pointing under a strict definition of crossing errors (6.23% vs. 1.94%). Subjective results indicate a preference for goal crossing among motor-impaired users. This work provides the empirical foundation from which to pursue the design of crossing-based interfaces as accessible alternatives to pointing-based interfaces.

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

[2]  B Phillips,et al.  Predictors of assistive technology abandonment. , 1993, Assistive technology : the official journal of RESNA.

[3]  Peter Robinson,et al.  Cursor measures for motion-impaired computer users , 2002, ASSETS.

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

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

[6]  A. D. Fisk,et al.  Age-related differences in movement control: adjusting submovement structure to optimize performance. , 1997, The journals of gerontology. Series B, Psychological sciences and social sciences.

[7]  Tovi Grossman,et al.  The bubble cursor: enhancing target acquisition by dynamic resizing of the cursor's activation area , 2005, CHI.

[8]  Marti L. Riemer-Reiss,et al.  Factors Associated with Assistive Technology Discontinuance among Individuals with Disabilities , 2000 .

[9]  Krishna Bharat,et al.  Making computers easier for older adults to use: area cursors and sticky icons , 1997, CHI.

[10]  François Guimbretière,et al.  Techniques , 2011, Laboratory Investigation.

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

[12]  Shumin Zhai,et al.  More than dotting the i's --- foundations for crossing-based interfaces , 2002, CHI.

[13]  J. Duysens,et al.  Children with congenital spastic hemiplegia obey Fitts’ Law in a visually guided tapping task , 2007, Experimental Brain Research.

[14]  I. Scott MacKenzie,et al.  Card, English, and Burr (1978): 25 years later , 2003, CHI Extended Abstracts.

[15]  Shumin Zhai,et al.  Beyond Fitts' law: models for trajectory-based HCI tasks , 1997, CHI Extended Abstracts.

[16]  Abigail Sellen,et al.  A comparison of input devices in element pointing and dragging tasks , 1991, CHI.

[17]  Brad A. Myers,et al.  From letters to words: efficient stroke-based word completion for trackball text entry , 2006, Assets '06.

[18]  S. Mackenzie,et al.  A comparison of input device in elemental pointing and dragging task , 1991, CHI 1991.

[19]  C. Schuster,et al.  The Relationship of ANOVA Models with Random Effects and Repeated Measurement Designs , 2001 .

[20]  Alison Gump,et al.  Application of Fitts' Law to Individuals with Cerebral Palsy , 2002, Perceptual and motor skills.

[21]  Patrick Langdon,et al.  Multiple haptic targets for motion-impaired computer users , 2003, CHI '03.

[22]  Parlette Gn 25 years later. , 1976, Journal of environmental health.

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

[24]  Renaud Blanch,et al.  Semantic pointing: improving target acquisition with control-display ratio adaptation , 2004, CHI.

[25]  George E Stelmach,et al.  Age-related kinematic differences as influenced by task difficulty, target size, and movement amplitude. , 2002, The journals of gerontology. Series B, Psychological sciences and social sciences.

[26]  Simeon Keates,et al.  Developing steady clicks:: a method of cursor assistance for people with motor impairments , 2006, Assets '06.

[27]  Brigitte N. Frederick Fixed-, Random-, and Mixed-Effects ANOVA Models: A User-Friendly Guide for Increasing the Generalizability of ANOVA Results. , 1999 .

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

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

[30]  Edmund F. LoPresti,et al.  Neck range of motion and use of computer head controls , 2000, Assets '00.