research-article

iGrasp: grasp-based adaptive keyboard for mobile devices

Authors:

Lung-Pan Cheng,

Hsiang-Sheng Liang,

Mike Y. ChenAuthors Info & Claims

CHI '13: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 3037 - 3046

https://doi.org/10.1145/2470654.2481422

Published: 27 April 2013 Publication History

Abstract

Multitouch tablets, such as iPad and Android tablets, support virtual keyboards for text entry. Our 64-user study shows that 98% of the users preferred different keyboard layouts and positions depending on how they were holding these devices. However, current tablets either do not allow keyboard adjustment or require users to manually adjust the keyboards. We present iGrasp, which automatically adapts the layout and position of virtual keyboards based on how and where users are grasping the devices without requiring explicit user input. Our prototype uses 46 capacitive sensors positioned along the sides of an iPad to sense users' grasps, and supports two types of grasp-based automatic adaptation: layout switching and continuous positioning. Our two 18-user studies show that participants were able to begin typing 42% earlier using iGrasp's adaptive keyboard compared to the manually adjustable keyboard. Participants also rated iGrasp much easier to use than the manually adjustable keyboard (4.2 vs 2.9 on five-point Likert scale.)

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Index Terms

iGrasp: grasp-based adaptive keyboard for mobile devices
1. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Conferences

CHI '13: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2013

3550 pages

ISBN:9781450318990

DOI:10.1145/2470654

General Chair:
Wendy E. Mackay
INRIA
,
Program Chairs:
Stephen Brewster
Glasgow University
,
Susanne Bødker
University of Aarhus

Copyright © 2013 ACM.

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Published: 27 April 2013

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CHI '13: CHI Conference on Human Factors in Computing Systems

April 27 - May 2, 2013

Paris, France

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CHI '13 Paper Acceptance Rate 392 of 1,963 submissions, 20%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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https://dl.acm.org/doi/10.1145/3544548.3581300
Li ZYu CGu YShi Y(2023)ResType: Invisible and Adaptive Tablet Keyboard Leveraging Resting FingersProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581055(1-14)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581055
Zhang XIkematsu KKato KSugiura Y(2022)Evaluation of Grasp Posture Detection Method using Corneal Reflection Images through a Crowdsourced ExperimentCompanion Proceedings of the 2022 Conference on Interactive Surfaces and Spaces10.1145/3532104.3571457(9-13)Online publication date: 20-Nov-2022
https://dl.acm.org/doi/10.1145/3532104.3571457
Zhang XIkematsu KKato KSugiura Y(2022)ReflecTouch: Detecting Grasp Posture of Smartphone Using Corneal Reflection ImagesProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517440(1-8)Online publication date: 29-Apr-2022
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Le HMayer SSteuerlein BHenze N(2019)Investigating Unintended Inputs for One-Handed Touch Interaction Beyond the TouchscreenProceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services10.1145/3338286.3340145(1-14)Online publication date: 1-Oct-2019
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