Home > Research > Publications & Outputs > Classifying Head Movements to Separate Head-Gaz...

Electronic data

Links

Text available via DOI:

View graph of relations

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

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
Publication date19/03/2023
Host publicationProceedings of the 2023 CHI Conference on Human Factors in Computing
Place of PublicationNew York
PublisherACM
Pages253:1-253:14
Number of pages14
ISBN (electronic)9781450394215
<mark>Original language</mark>English

Abstract

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.