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A Study on Grasp Recognition Independent of Users' Situations Using Built-in Sensors of Smartphones

Authors:

Chanho Park,

Takefumi OgawaAuthors Info & Claims

UIST '15 Adjunct: Adjunct Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology

Pages 69 - 70

https://doi.org/10.1145/2815585.2815722

Published: 06 November 2015 Publication History

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Abstract

There are many hand postures of smartphone according to the users? situations. In order to support appropriate inter-face, it is important to know user?s hand posture. To recognize grasp postures, which is not depend on users? situations, we consider using smartphone?s touchscreen and their built-in gyroscope and accelerometer and use support vector machine (SVM). In order to evaluate our system, we described the result of the experiments when users are using the devices in the room and on the train. We knew that our system could be feasible for personal use only system by improving the information from the accelerometer. We also collected users? data when users are sitting in the room. Results showed that grasp recognition accuracy under 5 and 4 hand postures were 87.7%, 92.4% respectively when training and testing on 6 users.

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Hörschinger RKurz MSonnleitner E(2024)Know Your Grip: Real-Time Holding Posture Recognition for SmartphonesElectronics10.3390/electronics1323459613:23(4596)Online publication date: 21-Nov-2024
https://doi.org/10.3390/electronics13234596
Zieher TProbst K(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
https://dl.acm.org/doi/10.1145/3676508
Wang RHuang LWang C(2024)Fast Detection of Handheld Phone-distracted Driving by Sensing the Driver's Hand-gripIEEE Transactions on Vehicular Technology10.1109/TVT.2024.3374589(1-13)Online publication date: 2024
https://doi.org/10.1109/TVT.2024.3374589
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Index Terms

A Study on Grasp Recognition Independent of Users' Situations Using Built-in Sensors of Smartphones
1. Human-centered computing
  1. Human computer interaction (HCI)

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Information & Contributors

Information

Published In

UIST '15 Adjunct: Adjunct Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology

November 2015

124 pages

ISBN:9781450337809

DOI:10.1145/2815585

General Chair:
Celine Latulipe
UNC Charlotte, USA
,
Program Chairs:
Bjoern Hartmann
UC Berkeley, USA
,
Tovi Grossman
Autodesk Research, Canada

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2015

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Poster

Funding Sources

the Japan Society for the Promotion of Science

Conference

UIST '15

Sponsor:

UIST '15: The 28th Annual ACM Symposium on User Interface Software and Technology

November 8 - 11, 2015

Kyungpook, Daegu, Republic of Korea

Acceptance Rates

Overall Acceptance Rate 355 of 1,733 submissions, 20%

Upcoming Conference

UIST '25

Sponsor:
sigchi
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The 38th Annual ACM Symposium on User Interface Software and Technology

September 28 - October 1, 2025

Busan , Republic of Korea

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Cited By

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Hörschinger RKurz MSonnleitner E(2024)Know Your Grip: Real-Time Holding Posture Recognition for SmartphonesElectronics10.3390/electronics1323459613:23(4596)Online publication date: 21-Nov-2024
https://doi.org/10.3390/electronics13234596
Zieher TProbst K(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
https://dl.acm.org/doi/10.1145/3676508
Wang RHuang LWang C(2024)Fast Detection of Handheld Phone-distracted Driving by Sensing the Driver's Hand-gripIEEE Transactions on Vehicular Technology10.1109/TVT.2024.3374589(1-13)Online publication date: 2024
https://doi.org/10.1109/TVT.2024.3374589
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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Chen TZhu KYang M(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
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Liang CYu CWei XXu XHu YWang YShi YKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Auth+Track: Enabling Authentication Free Interaction on Smartphone by Continuous User TrackingProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445624(1-16)Online publication date: 6-May-2021
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Yu CWei XVachher SQin YLiang CWeng YGu YShi YBrewster SFitzpatrick GCox AKostakos V(2019)HandSeeProceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300935(1-13)Online publication date: 2-May-2019
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Kim NLee JWhang JLee J(2019)SmartGrip: grip sensing system for commodity mobile devices through sound signalsPersonal and Ubiquitous Computing10.1007/s00779-019-01337-7Online publication date: 13-Nov-2019
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Recommendations

GripSense: using built-in sensors to detect hand posture and pressure on commodity mobile phones

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