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Diagnosis Interface
Acquired brain injury Physical keyboard
  • Summary

    Basic Stats for These Cases
    Cases Mean SD Range
    18 7.07wpm 4.01 2.50 to 18.60

    The histogram below shows the distribution of text entry rate across these 18 cases. Click on a datapoint to view the abstract for its study.

  • Explore

    This section displays a variety of views of the retrieved data, including how text entry rate varies with interface, diagnosis, or body site, where applicable. Note: These graphs combine data from the individual cases and groups, so they reflect all of the retrieved data.

    Text Entry Rate by Interface. Shows average for each interface in the retrieved dataset.

    Everyone in this dataset used physical keyboard, so we won't show a graph. The average text entry rate is 7.07 wpm, across all cases and groups.

    Text Entry Rate by Diagnosis. Shows average for each diagnosis in the retrieved dataset.

    Text Entry Rate by Body Site. Shows average for each body site in the retrieved dataset.

  • Individual Data. Each row is a case for a single subject in the dataset.

    Study Subject Age Gender Diagnosis Body site Word prediction? TER (wpm)
    Tam 2009 98 10 F acquired_brain_injury_other hands_unspecified Y 7.50
    Koester 2015 148 50 F brain_tumor fingers_bilateral N 7.60
    Koester 2015 148 50 F brain_tumor fingers_bilateral N 7.20
    Tam 2009 93 13 F brain_tumor hands_unspecified Y 7.50
    Koester 2015 154 62 M CVA finger_bilateral N 7.00
    Koester 2015 154 62 M CVA finger_bilateral N 6.60
    Koester 2004 30 46 F CVA fingers_bilateral N 18.60
    Tumlin 2004 23 17 M CVA hands_unspecified N 14.00
    Tumlin 2004 23 17 M CVA hands_unspecified Y 10.40
    Koester 2015 155 19 M TBI finger_bilateral N 2.90
    Koester 2015 155 19 M TBI finger_bilateral N 3.00
    Koester 2007 57 42 M TBI finger_unilateral N 5.56
    Koester 2007 57 42 M TBI finger_unilateral N 5.76
    Koester 2007 57 42 M TBI finger_unilateral N 5.36
    Kelway 2018 190 60 M TBI finger_unilateral N 4.40
    Kelway 2018 190 60 M TBI finger_unilateral N 7.60
    Tumlin 2004 20 21 M TBI hands_unspecified N 2.50
    Tumlin 2004 20 21 M TBI hands_unspecified Y 3.80


  • The list below shows all studies in the dataset that match your search criteria.

    • Using Word Prediction Software to Increase Typing Fluency with Students with Physical Disabilities Garrett J, Heller K. (2004) Journal of Special Education Technology, 19(3), 5-14. Show abstract
    • Usage, performance, and satisfaction outcomes for experienced users of automatic speech recognition Koester H. (2004) Journal of Rehabilitation Research & Development, 41(5), 739-754. Show abstract
    • Toward automatic adjustment of keyboard settings for people with physical impairments Koester H, LoPresti E, Simpson R. (2007) Disability and Rehabilitation: Assistive Technology, 2(5), 261-274. Show abstract
    • Evaluating the benefits of displaying word prediction lists on a personal digital assistant at the keyboard level Tam C, Wells D. (2009) Assistive Technology, 21(3), 105-114. Show abstract
    • Automatic adjustment of keyboard settings can enhance typing Koester H, Mankowski J. (2015) Assistive Technology, 27(3), 136-146. Show abstract
    • Improving the Academic Inclusion of a Student with Special Needs at University Bordeaux Kelway J, Brock A, Guitton P, Millet A, Nakata Y. (2018) Proceedings of the 20th International ACM SIGACCESS Conference on Computers and Accessibility, 52-56. Show abstract

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