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

Baosheng James Hou; Joshua Newn; Ludwig Sidenmark; Anam Ahmad  Khan; Per Bækgaard; Hans Gellersen

doi:10.1145/3544548.3581201

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

Baosheng James Hou, Joshua Newn, Ludwig Sidenmark, Anam Ahmad Khan, Per Bækgaard, Hans Gellersen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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.

Original language	English
Title of host publication	Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Number of pages	14
Publisher	ACM
Publication date	2023
Article number	253
ISBN (Electronic)	978-1-4503-9421-5
DOIs	https://doi.org/10.1145/3544548.3581201
Publication status	Published - 2023
Event	2023 CHI Conference on Human Factors in Computing Systems - Hamburg, Germany Duration: 23 Apr 2023 → 28 Apr 2023

Conference

Conference	2023 CHI Conference on Human Factors in Computing Systems
Country/Territory	Germany
City	Hamburg
Period	23/04/2023 → 28/04/2023

Keywords

Head Gestures
Eye Tracking
Virtual Reality
Eye-head coordination
Computational Interaction
Machine Learning
XGBoost

Access to Document

10.1145/3544548.3581201

OpenUrl availability

Full text

Cite this

@inproceedings{ebd456e651654efdb4ad50a0a8fb5ba9,

title = "Classifying Head Movements to Separate Head-Gaze and Head Gestures as Distinct Modes of Input",

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.",

keywords = "Head Gestures, Eye Tracking, Virtual Reality, Eye-head coordination, Computational Interaction, Machine Learning, XGBoost",

author = "Hou, {Baosheng James} and Joshua Newn and Ludwig Sidenmark and Khan, {Anam Ahmad} and Per B{\ae}kgaard and Hans Gellersen",

year = "2023",

doi = "10.1145/3544548.3581201",

language = "English",

booktitle = "Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems",

publisher = "ACM",

note = "2023 CHI Conference on Human Factors in Computing Systems, CHI ; Conference date: 23-04-2023 Through 28-04-2023",

}

Hou, BJ, Newn, J, Sidenmark, L, Khan, AA, Bækgaard, P & Gellersen, H 2023, Classifying Head Movements to Separate Head-Gaze and Head Gestures as Distinct Modes of Input. in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems., 253, ACM, 2023 CHI Conference on Human Factors in Computing Systems, Hamburg, Schleswig-Holstein, Germany, 23/04/2023. https://doi.org/10.1145/3544548.3581201

TY - GEN

T1 - Classifying Head Movements to Separate Head-Gaze and Head Gestures as Distinct Modes of Input

AU - Hou, Baosheng James

AU - Newn, Joshua

AU - Sidenmark, Ludwig

AU - Khan, Anam Ahmad

AU - Bækgaard, Per

AU - Gellersen, Hans

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Head Gestures

KW - Eye Tracking

KW - Virtual Reality

KW - Eye-head coordination

KW - Computational Interaction

KW - Machine Learning

KW - XGBoost

U2 - 10.1145/3544548.3581201

DO - 10.1145/3544548.3581201

M3 - Article in proceedings

BT - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

PB - ACM

T2 - 2023 CHI Conference on Human Factors in Computing Systems

Y2 - 23 April 2023 through 28 April 2023

ER -

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

Abstract

Conference

Keywords

Access to Document

OpenUrl availability

Fingerprint

Cite this