TapGazer: Text Entry with finger tapping and gaze-directed word selection

Zhenyi He; Christof Lutteroth; Ken Perlin

doi:10.1145/3491102.3501838

TapGazer: Text Entry with finger tapping and gaze-directed word selection

Zhenyi He, Christof Lutteroth, Ken Perlin

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

19 Citations (SciVal)

788 Downloads (Pure)

Abstract

While using VR, efficient text entry is a challenge: users cannot easily locate standard physical keyboards, and keys are often out of reach, e.g. when standing. We present TapGazer, a text entry system where users type by tapping their fingers in place. Users can tap anywhere as long as the identity of each tapping finger can be detected with sensors. Ambiguity between different possible input words is resolved by selecting target words with gaze. If gaze tracking is unavailable, ambiguity is resolved by selecting target words with additional taps. We evaluated TapGazer for seated and standing VR: seated novice users using touchpads as tap surfaces reached 44.81 words per minute (WPM), 79.17% of their QWERTY typing speed. Standing novice users tapped on their thighs with touch-sensitive gloves, reaching 45.26 WPM (71.91%). We analyze TapGazer with a theoretical performance model and discuss its potential for text input in future AR scenarios.

Original language	English
Title of host publication	CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450391573
DOIs	https://doi.org/10.1145/3491102.3501838
Publication status	Published - 29 Apr 2022

Publication series

Name	Conference on Human Factors in Computing Systems - Proceedings

Bibliographical note

Funding Information:
This research was supported by NSF grant 1626098 and a grant from the Verizon Corporation. We thank all our study participants.

Keywords

Eye Tracking
Input Techniques
Text Entry
Typing
Virtual Reality

ASJC Scopus subject areas

Human-Computer Interaction
Computer Graphics and Computer-Aided Design
Software

Access to Document

10.1145/3491102.3501838

TapGazer__CHI_22_PDF
© ACM, 2022. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, {April 2022} http://doi.acm.org/10.1145/3491102.3501838
Accepted author manuscript, 5.76 MB

Cite this

TapGazer: Text Entry with finger tapping and gaze-directed word selection. / He, Zhenyi; Lutteroth, Christof; Perlin, Ken.
CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2022. 337 (Conference on Human Factors in Computing Systems - Proceedings).

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

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title = "TapGazer: Text Entry with finger tapping and gaze-directed word selection",

abstract = "While using VR, efficient text entry is a challenge: users cannot easily locate standard physical keyboards, and keys are often out of reach, e.g. when standing. We present TapGazer, a text entry system where users type by tapping their fingers in place. Users can tap anywhere as long as the identity of each tapping finger can be detected with sensors. Ambiguity between different possible input words is resolved by selecting target words with gaze. If gaze tracking is unavailable, ambiguity is resolved by selecting target words with additional taps. We evaluated TapGazer for seated and standing VR: seated novice users using touchpads as tap surfaces reached 44.81 words per minute (WPM), 79.17% of their QWERTY typing speed. Standing novice users tapped on their thighs with touch-sensitive gloves, reaching 45.26 WPM (71.91%). We analyze TapGazer with a theoretical performance model and discuss its potential for text input in future AR scenarios.",

keywords = "Eye Tracking, Input Techniques, Text Entry, Typing, Virtual Reality",

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AU - He, Zhenyi

AU - Lutteroth, Christof

AU - Perlin, Ken

N1 - Funding Information: This research was supported by NSF grant 1626098 and a grant from the Verizon Corporation. We thank all our study participants.

PY - 2022/4/29

Y1 - 2022/4/29

N2 - While using VR, efficient text entry is a challenge: users cannot easily locate standard physical keyboards, and keys are often out of reach, e.g. when standing. We present TapGazer, a text entry system where users type by tapping their fingers in place. Users can tap anywhere as long as the identity of each tapping finger can be detected with sensors. Ambiguity between different possible input words is resolved by selecting target words with gaze. If gaze tracking is unavailable, ambiguity is resolved by selecting target words with additional taps. We evaluated TapGazer for seated and standing VR: seated novice users using touchpads as tap surfaces reached 44.81 words per minute (WPM), 79.17% of their QWERTY typing speed. Standing novice users tapped on their thighs with touch-sensitive gloves, reaching 45.26 WPM (71.91%). We analyze TapGazer with a theoretical performance model and discuss its potential for text input in future AR scenarios.

AB - While using VR, efficient text entry is a challenge: users cannot easily locate standard physical keyboards, and keys are often out of reach, e.g. when standing. We present TapGazer, a text entry system where users type by tapping their fingers in place. Users can tap anywhere as long as the identity of each tapping finger can be detected with sensors. Ambiguity between different possible input words is resolved by selecting target words with gaze. If gaze tracking is unavailable, ambiguity is resolved by selecting target words with additional taps. We evaluated TapGazer for seated and standing VR: seated novice users using touchpads as tap surfaces reached 44.81 words per minute (WPM), 79.17% of their QWERTY typing speed. Standing novice users tapped on their thighs with touch-sensitive gloves, reaching 45.26 WPM (71.91%). We analyze TapGazer with a theoretical performance model and discuss its potential for text input in future AR scenarios.

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TapGazer: Text Entry with finger tapping and gaze-directed word selection

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA) - 2.0

Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA)

Cite this

TapGazer: Text Entry with finger tapping and gaze-directed word selection

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Projects

Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA) - 2.0

Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA)

Cite this