• DocumentCode
    77225
  • Title

    GyroPen: Gyroscopes for Pen-Input With Mobile Phones

  • Author

    Deselaers, Thomas ; Keysers, Daniel ; Hosang, Jan ; Rowley, Henry A.

  • Author_Institution
    Google Inc., Mountain View, CA, USA
  • Volume
    45
  • Issue
    2
  • fYear
    2015
  • fDate
    Apr-15
  • Firstpage
    263
  • Lastpage
    271
  • Abstract
    We present GyroPen, a method to reconstruct the motion path for pen-like interaction from standard built-in sensors in modern smartphones. The key idea is to reconstruct a representation of the trajectory of the phone´s corner that is touching a writing or drawing surface from the measurements obtained from the phone´s gyroscopes and accelerometers. We propose to directly use the angular trajectory for this reconstruction, which removes the necessity for accurate absolute 3-D position estimation, a task that can be difficult using low-cost accelerometers. We connect GyroPen to a handwriting recognition system and perform two proof-of-concept experiments to demonstrate that the reconstruction accuracy of GyroPen is accurate enough to be a promising approach to text entry. In a first experiment, the average novice participant (n=10) was able to write the first word only 37 s after the starting to use GyroPen for the first time. In a second experiment, experienced users (n=2) were able to write at the speed of 3-4 s for one English word and with a character error rate of 18%.
  • Keywords
    accelerometers; gesture recognition; gyroscopes; handwriting recognition; interactive devices; sensor fusion; smart phones; absolute 3D position estimation; angular trajectory; average novice participant; character error rate; gyropen; gyroscopes; handwriting recognition system; low-cost accelerometers; mobile phones; pen-input; pen-like interaction; proof-of-concept experiments; reconstruction accuracy; smart-phones; standard built-in sensors; surface drawing; text entry; Accelerometers; Gravity; Gyroscopes; Handwriting recognition; Mobile handsets; Sensors; Writing; Computer and information processing; gesture recognition; handwriting recognition; pattern recognition; text recognition;
  • fLanguage
    English
  • Journal_Title
    Human-Machine Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    2168-2291
  • Type

    jour

  • DOI
    10.1109/THMS.2014.2365723
  • Filename
    6975206