Integrating Blink Click interaction into a head tracking system: implementation and usability issues

In the area of human–computer interaction, contemporary head tracking systems are often used as camera-based mouse emulators. While head movement detection provides the basis for related mouse shifting and positioning, standard click actions are usually emulated using stillness counter techniques such as Dwell Click (DC). However, these techniques can be a source of enlarged interaction burden, as users often have to struggle with time-consuming repetitive UI actions. This paper proposes a novel version of Blink Click (BC) action called B2C, based on double eye blink detection, as a valuable supplement for faster mouse click emulation. The integration of the proposed BC action into an existing head tracking system is presented, and implementation issues are thoroughly analyzed. Usability testing of the proposed B2C interaction model, along with the already embedded DC model, has been carried out, providing both quantitative and qualitative outcomes. The results show efficiency improvement as well as a higher level of users’ satisfaction when using the proposed version of BC, thus making it a strong candidate to become a standard feature within the computer-vision-based mouse emulation.

[1]  Arnold Baca,et al.  Editorial , 1999, Int. J. Comput. Sci. Sport.

[2]  Gavriel Salvendy,et al.  Number of people required for usability evaluation , 2010, Commun. ACM.

[3]  B. Shneiderman Universal Usability: Pushing Human-Computer Interaction Research to Empower Every Citizen , 1999 .

[4]  J. Border,et al.  Exploring Empirical Guidelines for Selecting Computer Assistive Technology for People with Disabilities , 2011 .

[5]  Emiliano Miluzzo,et al.  EyePhone: activating mobile phones with your eyes , 2010, MobiHeld '10.

[6]  SalvendyGavriel,et al.  Number of people required for usability evaluation , 2010 .

[7]  Richard E A Bates Enhancing the Performance of Eye and Head Mice: A Validated Assessment Method and an Investigation into the Performance of Eye and Head Based Assistive Technology Pointing Devices , 2006 .

[8]  Constantine Stephanidis,et al.  Universal Access in the Information Society: Methods, Tools, and Interaction Technologies , 2001, Universal Access in the Information Society.

[9]  Toni Granollers,et al.  COMPUTER VISION INTERACTION FOR PEOPLE WITH SEVERE MOVEMENT RESTRICTIONS , 2006 .

[10]  Jorge Batista,et al.  A Drowsiness and Point of Attention Monitoring System for Driver Vigilance , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[11]  Heiko Drewes,et al.  Eye gaze tracking for human computer interaction , 2010 .

[12]  I. Scott MacKenzie,et al.  BlinkWrite2: an improved text entry method using eye blinks , 2010, ETRA '10.

[13]  Pawel Strumillo,et al.  Eye-blink detection system for human–computer interaction , 2011, Universal Access in the Information Society.

[14]  Margrit Betke,et al.  Communication via eye blinks and eyebrow raises: video-based human-computer interfaces , 2003, Universal Access in the Information Society.

[15]  Antoine Picot,et al.  Comparison between EOG and high frame rate camera for drowsiness detection , 2009, 2009 Workshop on Applications of Computer Vision (WACV).

[16]  Mario Gerla,et al.  EyeGuardian: a framework of eye tracking and blink detection for mobile device users , 2012, HotMobile '12.

[17]  Margrit Betke,et al.  Real Time Eye Tracking and Blink Detection with USB Cameras , 2005 .

[18]  Ben Shneiderman,et al.  Universal usability , 2000, Commun. ACM.

[19]  Margrit Betke,et al.  Blink and wink detection for mouse pointer control , 2010, PETRA '10.

[20]  Mubarak Shah,et al.  Monitoring head/eye motion for driver alertness with one camera , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.