Advertisement

How Can Adding a Movement Improve Target Acquisition Efficacy?

  • Alexander R. PayneEmail author
  • Beryl Plimmer
  • Andrew McDaid
  • Andrew Luxton-Reilly
  • T. Claire Davies
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10515)

Abstract

People with motor impairments, such as cerebral palsy (CP), have difficulty acquiring small targets with a mouse. To improve upon this many assistive technologies enlarge targets, generally by introducing an extra movement. Often this improves accuracy however there appears to be a time penalty of performing all aspects of a movement twice. We investigate if it is possible for an extra movement to improve efficacy, and if this time penalty can be counterbalanced by a reduction of errors and corrective movements.

We measure overall interaction efficacy in a controlled 1D experiment. Participants acquire targets under three conditions: a single movement, a double movement, and using an example assistive tool. We anticipate that a double movement may only increase efficacy when the single movement target is so small that corrective movements are disproportionately time consuming. Therefore we investigated the effects of task scale, and of motor control. The results show that it is possible for two movements to be more efficient than one. However, this appears to be an edge case that only occurs at a very small scale. We suggest that tool designers must focus on how and why their tool is going to be attractive to users, since in real world situations it is unlikely to improve pointing efficacy. Users may choose to use a tool because it improves accuracy, or requires less effort, but they are unlikely to use it because it is faster.

Keywords

Cerebral palsy Motor impairment Breadth Depth Fitts’s law Mouse pointing Target size 

Supplementary material

421764_1_En_34_MOESM1_ESM.docx (108 kb)
Supplementary material 1 (docx 107 KB)

References

  1. 1.
    Chapuis, O., Dragicevic, P.: Effects of motor scale, visual scale, and quantization on small target acquisition difficulty. ACM Trans. Comput.-Hum. Interact. 18, 13 (2011)CrossRefGoogle Scholar
  2. 2.
    Davies, T.C., AlManji, A., Stott, N.S.: A cross-sectional study examining computer task completion by adolescents with cerebral palsy across the manual ability classification system levels. Dev. Med. Child Neurol. 56, 1180–1186 (2014)CrossRefGoogle Scholar
  3. 3.
    Almanji, A., Payne, A., Amor, R., Davies, C.: A nonlinear model for mouse pointing task movement time analysis based on both system and human effects. IEEE Trans. Neural Syst. Rehabil. Eng. 23(6), 1003–1011 (2014)CrossRefGoogle Scholar
  4. 4.
    Bakaev M.: Fitts’ law for older adults: considering a factor of age, pp. 260–263 (2008)Google Scholar
  5. 5.
    Findlater, L., Jansen, A., Shinohara, K., Dixon, M., Kamb, P., Rakita, J., Wobbrock, J.O.: Enhanced area cursors: reducing fine pointing demands for people with motor impairments, pp. 153–162 (2010)Google Scholar
  6. 6.
    Payne, A.R., Plimmer, B., McDaid, A., Luxton-Reilly, A., Davies, T.C.: Expansion cursor: a zoom lens that can be voluntarily activated by the user at every individual click, pp. 81–90 (2016)Google Scholar
  7. 7.
    Li, L., Gajos, K.Z.: Adaptive click-and-cross: adapting to both abilities and task improves performance of users with impaired dexterity, pp. 299–304 (2014)Google Scholar
  8. 8.
    Jansen, A., Findlater, L., Wobbrock, J.O.: From the lab to the world: lessons from extending a pointing technique for real-world use, pp. 1867–1872 (2011)Google Scholar
  9. 9.
    Kiger, J.I.: The depth/breadth trade-off in the design of menu-driven user interfaces. Int. J. Man-Mach. Stud. 20, 201–213 (1984)CrossRefGoogle Scholar
  10. 10.
    Landauer, T.K., Nachbar, D.: Selection from alphabetic and numeric menu trees using a touch screen: breadth, depth, and width. ACM SIGCHI Bull. 16, 73–78 (1985)CrossRefGoogle Scholar
  11. 11.
    Zaphiris, P., Shneiderman, B., Norman, K.L.: Expandable indexes vs. sequential menus for searching hierarchies on the world wide web. Behav. Inf. Technol. 21, 201–207 (2002)CrossRefGoogle Scholar
  12. 12.
    Hwang, F., Keates, S., Langdon, P., Clarkson, J.: Mouse movements of motion-impaired users: a submovement analysis, pp. 102–109 (2004)Google Scholar
  13. 13.
    Hwang, F., Keates, S., Langdon, P., Clarkson, J.: A submovement analysis of cursor trajectories. Behav. Inf. Technol. 24, 205–217 (2005)CrossRefGoogle Scholar
  14. 14.
    Saavedra, S., Joshi, A., Woollacott, M., Van Donkelaar, P.: Eye hand coordination in children with cerebral palsy. Exp. Brain Res. 192, 155–165 (2009)CrossRefGoogle Scholar
  15. 15.
    Seow, S.C.: Information theoretic models of HCI: a comparison of the hick-hyman law and fitts’ law. Hum.-Comput. Interact. 20, 315–352 (2005)CrossRefGoogle Scholar
  16. 16.
    Fitts, P.M.: The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol. 47, 381 (1954)CrossRefGoogle Scholar
  17. 17.
    MacKenzie, I.S.: Fitts’ law as a research and design tool in human-computer interaction. Hum.-Comput. Interact. 7, 91–139 (1992)CrossRefGoogle Scholar
  18. 18.
    Card, S.K., English, W.K., Burr, B.J.: Evaluation of mouse, rate-controlled isometric joystick, step keys, and text keys for text selection on a CRT. Ergonomics 21, 601–613 (1978)CrossRefGoogle Scholar
  19. 19.
    Bax, M.C.: Terminology and classification of cerebral palsy. Dev. Med. Child Neurol. 6, 295–297 (1964)CrossRefGoogle Scholar
  20. 20.
    Rosenbaum, P., Paneth, N., Leviton, A., Goldstein, M., Bax, M., Damiano, D., Dan, B., Jacobsson, B.: A report: the definition and classification of cerebral palsy april 2006. Dev. Med. Child Neurol. Suppl. 109, 8–14 (2007)Google Scholar
  21. 21.
    Ramos, G., Cockburn, A., Balakrishnan, R., Beaudouin-Lafon, M.: Pointing lenses: facilitating stylus input through visual-and motor-space magnification, pp. 757–766 (2007)Google Scholar
  22. 22.
    Zhang, X., Zha, H., Feng, W.: Extending Fitts’ law to account for the effects of movement direction on 2D pointing, pp. 3185–3194 (2012)Google Scholar
  23. 23.
    Eliasson, A., Krumlinde-Sundholm, L., Rösblad, B., Beckung, E., Arner, M., Öhrvall, A., Rosenbaum, P.: The manual ability classification system (MACS) for children with cerebral palsy: scale development and evidence of validity and reliability. Dev. Med. Child neurol. 48, 549–554 (2006)CrossRefGoogle Scholar
  24. 24.
    NASA: NASA TLX: Task load index (2016). http://humansystems.arc.nasa.gov/groups/TLX/. Accessed July 2016
  25. 25.
    Smits-Engelsman, B.C.M., Rameckers, E.A.A., Duysens, J.: Children with congenital spastic hemiplegia obey Fitts’ law in a visually guided tapping task. Exp. Brain Res. 177, 431–439 (2007)CrossRefGoogle Scholar
  26. 26.
    Payne, A.R., Plimmer, B., Davies, T.C.: Repeatability of eye-hand movement onset asynchrony measurements and cerebral palsy: a case study, pp. 31–38 (2015)Google Scholar
  27. 27.
    Al Manji, A., Davies, C., Amor, R.: Examining dynamic control-display gain adjustments to assist mouse-based pointing for youths with cerebral palsy. J. Virtual Worlds Hum. Comput. Interact. 3, 1–9 (2015)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • Alexander R. Payne
    • 1
    Email author
  • Beryl Plimmer
    • 1
  • Andrew McDaid
    • 1
  • Andrew Luxton-Reilly
    • 1
  • T. Claire Davies
    • 2
  1. 1.University of AucklandAucklandNew Zealand
  2. 2.Queen’s UniversityKingstonCanada

Personalised recommendations