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Classifying Head Movements to Separate Head-Gaze and Head Gestures as Distinct Modes of Input

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review



  • Bashing James Hou, Lancaster University
  • ,
  • Joshua Newn, Lancaster University
  • ,
  • Ludwig Sidenmark, Lancaster University
  • ,
  • Anam Ahmad Khan, National University of Science and Technology
  • ,
  • Per Bækgaard, Technical University of Denmark
  • ,
  • Hans Gellersen

Head movement is widely used as a uniform type of input for human-computer interaction. However, there are fundamental differences between head movements coupled with gaze in support of our visual system, and head movements performed as gestural expression. Both Head-Gaze and Head Gestures are of utility for interaction but differ in their affordances. To facilitate the treatment of Head-Gaze and Head Gestures as separate types of input, we developed HeadBoost as a novel classifier, achieving high accuracy in classifying gaze-driven versus gestural head movement (F1-Score: 0.89). We demonstrate the utility of the classifier with three applications: gestural input while avoiding unintentional input by Head-Gaze; target selection with Head-Gaze while avoiding Midas Touch by head gestures; and switching of cursor control between Head-Gaze for fast positioning and Head Gesture for refinement. The classification of Head-Gaze and Head Gesture allows for seamless head-based interaction while avoiding false activation.

Original languageEnglish
Title of host publicationCHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Article number253
ISBN (Electronic)9781450394215
Publication statusAccepted/In press - 1 Mar 2023

    Research areas

  • Computational Interaction, Eye Tracking, Eye-head Coordination, Head Gestures, Machine Learning, Virtual Reality, XGBoost

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