poster

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

Get Access

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.

References

[1]

Chang, C.-C., and Lin, C.-J. Libsvm: A library for support vector machines. ACM Transactions on Intelligient Systems and Technology, Vol. 2, No. 3, pp.27:1--27:27, 2011.

Digital Library

Google Scholar

[2]

Cheng, L.-P., Lee, M.-H., Wu, C.-Y., Hsiao, F.-i., Liu, Y.-t., Liang, H.-s., Chiu, Y.-C., Lee, M.-S., and Chen, M.-Y. iRotateGrasp: Automatic Screen Rotation based on Grasp of Mobile Devices. In Proc. CHI?13, ACM, 2013.

Digital Library

Google Scholar

[3]

Goel, M., Wobbrock, J.-O., and Patel, S.-N. GripSense: Using Built-in Sensors to Detect Hand Posture and Pressure on Commodity Mobile Phones. In Proc. UIST?12, ACM, 2012.

Digital Library

Google Scholar

[4]

Van Laerhoven, K., and Cakmakci, O. What shall we teach our pants? Wearable Computers, The Fourth International Symposium, pp77--83, 2000.

Digital Library

Google Scholar

Cited By

View all

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
https://dl.acm.org/doi/10.1145/3491102.3517440
Show More Cited By

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)

Recommendations

GripSense: using built-in sensors to detect hand posture and pressure on commodity mobile phones
UIST '12: Proceedings of the 25th annual ACM symposium on User interface software and technology

We introduce GripSense, a system that leverages mobile device touchscreens and their built-in inertial sensors and vibration motor to infer hand postures including one- or two-handed interaction, use of thumb or index finger, or use on a table. ...
Two-Phase Activity Recognition with Smartphone Sensors
NBIS '15: Proceedings of the 2015 18th International Conference on Network-Based Information Systems

The smartphone has become very popular and it is very useful in our daily life. A smartphone is equipped with a variety of sensors, which makes it very suitable in replacing other types of wearable sensors for activity recognition of the user. Many ...
Using Smartphone Inertial Measurement Unit for Analysis of Human Gait

The study was aimed at searching the characteristic features of a human gait during a conventionally used neurologic walking test with the help of sensors (3D accelerometer and gyroscope) in a smartphone mounted on the person's head. This allowed ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

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

Check for updates

Author Tags

Qualifiers

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 561 of 2,567 submissions, 22%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
263
Total Downloads

Downloads (Last 12 months)14
Downloads (Last 6 weeks)4

Reflects downloads up to 13 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

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
https://dl.acm.org/doi/10.1145/3491102.3517440
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
https://doi.org/10.1007/s11042-021-11844-6
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
https://dl.acm.org/doi/10.1145/3411764.3445624
Wang RHuang LWang C(2021)Preventing Handheld Phone Distraction for Drivers by Sensing the Gripping Hand2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)10.1109/MASS52906.2021.00058(410-418)Online publication date: Oct-2021
https://doi.org/10.1109/MASS52906.2021.00058
Avery JVogel DLank EMasson DRateau H(2019)Holding patternsProceedings of the 31st Conference on l'Interaction Homme-Machine10.1145/3366550.3372253(1-7)Online publication date: 10-Dec-2019
https://dl.acm.org/doi/10.1145/3366550.3372253
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
https://dl.acm.org/doi/10.1145/3290605.3300935
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
https://doi.org/10.1007/s00779-019-01337-7
Kubo YTakada RShizuki BTakahashi S(2017)Exploring Context-Aware User Interfaces for Smartphone-Smartwatch Cross-Device InteractionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/31309341:3(1-21)Online publication date: 11-Sep-2017
https://dl.acm.org/doi/10.1145/3130934

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

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

Two-Phase Activity Recognition with Smartphone Sensors

Using Smartphone Inertial Measurement Unit for Analysis of Human Gait