TTracker: Using Finger Detection to Improve Touch Typing Training - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2017

TTracker: Using Finger Detection to Improve Touch Typing Training

Elvin Kollie
  • Fonction : Auteur
  • PersonId : 1026413
Fernando Loizides
  • Fonction : Auteur
  • PersonId : 1026322
Thomas Hartley
  • Fonction : Auteur
  • PersonId : 1026323
Adam Worrallo
  • Fonction : Auteur
  • PersonId : 1026324

Résumé

Touch typing software teaches a user to use the correct finger combinations with the correct keyboard buttons. The ultimate goal is to teach the typist to type faster, more accurately and ergonomically correct. Our research presents the working prototype of a software and hardware setup that tracks not only the speed and accuracy of the correct buttons being pressed but also which fingers are used to press them; a dimension of training that has previously not been integrated into touch typing tutorials. We use novel technology (leap motion) to detect the accurate interaction between the user and the keyboard, giving precise feedback to the user in order for him or her to improve.
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Dates et versions

hal-01679825 , version 1 (10-01-2018)

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Elvin Kollie, Fernando Loizides, Thomas Hartley, Adam Worrallo. TTracker: Using Finger Detection to Improve Touch Typing Training. 16th IFIP Conference on Human-Computer Interaction (INTERACT), Sep 2017, Bombay, India. pp.469-472, ⟨10.1007/978-3-319-68059-0_53⟩. ⟨hal-01679825⟩
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