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HandSense: discriminating different ways of grasping and holding a tangible user interface

Published: 16 February 2009 Publication History

Abstract

As mobile and tangible devices are getting smaller and smaller it is desirable to extend the interaction area to their whole surface area. The HandSense prototype employs capacitive sensors for detecting when it is touched or held against a body part. HandSense is also able to detect in which hand the device is held, and how. The general properties of our approach were confirmed by a user study. HandSense was able to correctly classify over 80 percent of all touches, discriminating six different ways of touching the device (hold left/right, pick up left/right, pick up at top/bottom). This information can be used to implement or enhance implicit and explicit interaction with mobile phones and other tangible user interfaces. For example, graphical user interfaces can be adjusted to the user's handedness.

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  • (2024)Know Your Grip: Real-Time Holding Posture Recognition for SmartphonesElectronics10.3390/electronics1323459613:23(4596)Online publication date: 21-Nov-2024
  • (2024)Usability Optimization for Mobile Menu Design: An Empirical Study of Hand Grips and User PreferencesProceedings of the ACM on Human-Computer Interaction10.1145/36765088:MHCI(1-19)Online publication date: 24-Sep-2024
  • (2023)AdHocProx: Sensing Mobile, Ad-Hoc Collaborative Device Formations using Dual Ultra-Wideband RadiosProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581300(1-18)Online publication date: 19-Apr-2023
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  1. HandSense: discriminating different ways of grasping and holding a tangible user interface

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    cover image ACM Other conferences
    TEI '09: Proceedings of the 3rd International Conference on Tangible and Embedded Interaction
    February 2009
    407 pages
    ISBN:9781605584935
    DOI:10.1145/1517664
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Microsoft Research (USA)
    • Microsoft Research Cambridge (UK)
    • Nokia (Finland)
    • Microsoft Hardware (USA)

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 16 February 2009

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    Author Tags

    1. capacitive sensing
    2. grasp
    3. handedness
    4. input devices
    5. sensors
    6. touch

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    Overall Acceptance Rate 393 of 1,367 submissions, 29%

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    Cited By

    View all
    • (2024)Know Your Grip: Real-Time Holding Posture Recognition for SmartphonesElectronics10.3390/electronics1323459613:23(4596)Online publication date: 21-Nov-2024
    • (2024)Usability Optimization for Mobile Menu Design: An Empirical Study of Hand Grips and User PreferencesProceedings of the ACM on Human-Computer Interaction10.1145/36765088:MHCI(1-19)Online publication date: 24-Sep-2024
    • (2023)AdHocProx: Sensing Mobile, Ad-Hoc Collaborative Device Formations using Dual Ultra-Wideband RadiosProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581300(1-18)Online publication date: 19-Apr-2023
    • (2023)OmniSense: Exploring Novel Input Sensing and Interaction Techniques on Mobile Device with an Omni-Directional CameraProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580747(1-18)Online publication date: 19-Apr-2023
    • (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
    • (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
    • (2022)Deep-learning-based unobtrusive handedness prediction for one-handed smartphone interactionMultimedia Tools and Applications10.1007/s11042-021-11844-682:4(4941-4964)Online publication date: 18-Jan-2022
    • (2020)Imprint-Based Input Techniques for Touch-Based Mobile DevicesProceedings of the 19th International Conference on Mobile and Ubiquitous Multimedia10.1145/3428361.3428393(32-41)Online publication date: 22-Nov-2020
    • (2020)Impact of Hand Used on One-Handed Back-of-Device PerformanceProceedings of the ACM on Human-Computer Interaction10.1145/34273164:ISS(1-19)Online publication date: 4-Nov-2020
    • (2020)Gripmarks: Using Hand Grips to Transform In-Hand Objects into Mixed Reality InputProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376313(1-11)Online publication date: 21-Apr-2020
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