research-article

Evaluation of Grasp Posture Detection Method using Corneal Reflection Images through a Crowdsourced Experiment

Authors:

Kaori Ikematsu,

Yuta SugiuraAuthors Info & Claims

ISS '22: Companion Proceedings of the 2022 Conference on Interactive Surfaces and Spaces

Pages 9 - 13

https://doi.org/10.1145/3532104.3571457

Published: 30 November 2022 Publication History

Abstract

To achieve adaptive user interfaces (UI) for smartphones, researchers have been developing sensing methods to detect how a user is holding a smartphone. A variety of promising adaptive UIs have been demonstrated, such as those that automatically switch the displayed content and the position of interactive components in accordance with how the phone is being held. In this paper, we present a follow-up study on ReflecTouch, a state-of-the-art grasping posture detection method proposed by Zhang et al. that uses corneal reflection images captured by the front camera of a smartphone. We extend the previous work by investigating the performance of this method towards actual use and its potential challenges through a crowdsourced experiment with a large number of participants.

References

[1]

Jeff Avery, Daniel Vogel, Edward Lank, Damien Masson, and Hanae Rateau. 2019. Holding Patterns: Detecting Handedness with a Moving Smartphone at Pickup. In Proceedings of the 31st Conference on l’Interaction Homme-Machine(IHM ’19). Association for Computing Machinery, New York, NY, USA, Article 7, 7 pages. https://doi.org/10.1145/3366550.3372253

Digital Library

[2]

Lung-Pan Cheng, Hsiang-Sheng Liang, Che-Yang Wu, and Mike Y. Chen. 2013. iGrasp: Grasp-Based Adaptive Keyboard for Mobile Devices. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems(CHI ’13). Association for Computing Machinery, New York, NY, USA, 3037–3046. https://doi.org/10.1145/2470654.2481422

Digital Library

[3]

Jay L Devore. 2011. Probability and Statistics for Engineering and the Sciences. Cengage learning.

[4]

Leah Findlater, Joan Zhang, Jon E Froehlich, and Karyn Moffatt. 2017. Differences in Crowdsourced vs. Lab-based Mobile and Desktop Input Performance Data. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 6813–6824. https://doi.org/10.1145/3025453.3025820

Digital Library

[5]

Mayank Goel, Jacob Wobbrock, and Shwetak Patel. 2012. GripSense: Using Built-in Sensors to Detect Hand Posture and Pressure on Commodity Mobile Phones. In Proceedings of the 25th Annual ACM Symposium on User Interface Software and Technology(UIST ’12). Association for Computing Machinery, New York, NY, USA, 545–554. https://doi.org/10.1145/2380116.2380184

Digital Library

[6]

Michael Xuelin Huang, Jiajia Li, Grace Ngai, and Hong Va Leong. 2017. ScreenGlint: Practical, In-situ Gaze Estimation on Smartphones. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 2546–2557. https://doi.org/10.1145/3025453.3025794

Digital Library

[7]

Kaori Ikematsu, Haruna Oshima, Rachel Eardley, and Itiro Siio. 2020. Investigating How Smartphone Movement is Affected by Lying Down Body Posture. Proc. ACM Hum.-Comput. Interact. 4, ISS, Article 192 (nov 2020), 17 pages. https://doi.org/10.1145/3427320

Digital Library

[8]

Namhyun Kim, Junseong Lee, Joyce Jiyoung Whang, and Jinkyu Lee. 2020. SmartGrip: Grip Sensing System for Commodity Mobile Devices through Sound Signals. Personal and Ubiquitous Computing 24, 5 (Oct 2020), 643–654. https://doi.org/10.1007/s00779-019-01337-7

Digital Library

[9]

Hyunchul Lim, David Lin, Jessica Tweneboah, and Cheng Zhang. 2021. HandyTrak: Recognizing the Holding Hand on a Commodity Smartphone from Body Silhouette Images. The 34th Annual ACM Symposium on User Interface Software and Technology, 1210–1220. https://doi.org/10.1145/3472749.3474817

Digital Library

[10]

Markus Löchtefeld, Phillip Schardt, Antonio Krüger, and Sebastian Boring. 2015. Detecting users handedness for ergonomic adaptation of mobile user interfaces. In Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia (Linz, Austria) (MUM ’15). Association for Computing Machinery, New York, NY, USA, 245–249. https://doi.org/10.1145/2836041.2836066

Digital Library

[11]

Makoto Ono, Buntarou Shizuki, and Jiro Tanaka. 2013. Touch & Activate: Adding Interactivity to Existing Objects Using Active Acoustic Sensing. In Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology(UIST ’13). Association for Computing Machinery, New York, NY, USA, 31–40. https://doi.org/10.1145/2501988.2501989

Digital Library

[12]

Daniel M. Oppenheimer, Tom Meyvis, and Nicolas Davidenko. 2009. Instructional manipulation checks: Detecting satisficing to increase statistical power. Journal of Experimental Social Psychology 45, 4 (2009), 867–872. https://doi.org/10.1016/j.jesp.2009.03.009

[13]

Raphael Wimmer and Sebastian Boring. 2009. HandSense: Discriminating Different Ways of Grasping and Holding a Tangible User Interface. In Proceedings of the 3rd International Conference on Tangible and Embedded Interaction(TEI ’09). Association for Computing Machinery, New York, NY, USA, 359–362. https://doi.org/10.1145/1517664.1517736

Digital Library

[14]

Hyunjin Yoo, Jungwon Yoon, and Hyunsoo Ji. 2015. Index Finger Zone: Study on Touchable Area Expandability Using Thumb and Index Finger. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct(MobileHCI ’15). Association for Computing Machinery, New York, NY, USA, 803–810. https://doi.org/10.1145/2786567.2793704

Digital Library

[15]

Xiang Zhang, Kaori Ikematsu, Kunihiro Kato, and Yuta Sugiura. 2022. ReflecTouch: Detecting Grasp Posture of Smartphone Using Corneal Reflection Images. In CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 289, 8 pages. https://doi.org/10.1145/3491102.3517440

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Cited By

Liu JTversky BFeiner S(2023)Cueing Sequential 6DoF Rigid-Body Transformations in Augmented Reality2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)10.1109/ISMAR59233.2023.00050(356-365)Online publication date: 16-Oct-2023
https://doi.org/10.1109/ISMAR59233.2023.00050

Index Terms

Evaluation of Grasp Posture Detection Method using Corneal Reflection Images through a Crowdsourced Experiment
1. Human-centered computing
  1. Human computer interaction (HCI)

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

ISS '22: Companion Proceedings of the 2022 Conference on Interactive Surfaces and Spaces

November 2022

86 pages

ISBN:9781450393560

DOI:10.1145/3532104

Editors:
Craig Anslow,
Judy Kay

Copyright © 2022 ACM.

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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Published: 30 November 2022

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ISS '22: Conference on Interactive Surfaces and Spaces

November 20 - 23, 2022

Wellington, New Zealand

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Cited By

Liu JTversky BFeiner S(2023)Cueing Sequential 6DoF Rigid-Body Transformations in Augmented Reality2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)10.1109/ISMAR59233.2023.00050(356-365)Online publication date: 16-Oct-2023
https://doi.org/10.1109/ISMAR59233.2023.00050

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