demonstration

Efficient in-pocket detection with mobile phones

Authors:

Jun Yang,

Emmanuel Munguia-Tapia,

Simon GibbsAuthors Info & Claims

UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication

Pages 31 - 34

https://doi.org/10.1145/2494091.2494099

Published: 08 September 2013 Publication History

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Abstract

In this demonstration paper, we show a novel approach to detect the common placements of a mobile phone, such as "in pocket", "in bag" or "out of pocket or bag", from embedded proximity (IR) and light sensors. We use sensor data fusion and pattern recognition to extract distinct features from sensor signals and classify the boundaries among these three phone placement contexts. The detection results are demonstrated on a Samsung Tizen mobile phone.

References

[1]

Kawahara, Y., Kurasawa, H., and Morikawa, H. Recognizing user context using mobile handsets with acceleration sensors. In IEEE International Conference on Portable Information Devices (Portable'07) (2007), 1--5.

Crossref

Google Scholar

[2]

Miluzzo, E., Pap, M., Lane, N. D., Lu, H., and Campbell, A. T. Pocket, bag, hand etc. - automatically detecting phone context through discovery. In First International Workshop on Sensing for App Phones (PhoneSense'10) (2010).

Google Scholar

[3]

Wang, C.-K. Electronic Device Capable of Operating According to Detection of Environmental Light. US Patent Application No. 2007/0099574.

Google Scholar

[4]

Wikipedia. Lux. http://en.wikipedia.org/wiki/Lux.

Google Scholar

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Khan ISun SRowe WThompson AAl-Hourani ASithamparanathan K(2022)Comparison of classifiers for use case detection using onboard smartphone sensors2022 32nd International Telecommunication Networks and Applications Conference (ITNAC)10.1109/ITNAC55475.2022.9998423(261-266)Online publication date: 30-Nov-2022
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Breda JTrivedi AWijesundara CBovornkeeratiroj PIrwin DShenoy PTaneja J(2019)Hot or NotProceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3360322.3360856(41-50)Online publication date: 13-Nov-2019
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Index Terms

Efficient in-pocket detection with mobile phones
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems

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Information & Contributors

Information

Published In

UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication

September 2013

1608 pages

ISBN:9781450322157

DOI:10.1145/2494091

General Chairs:
Friedemann Mattern
ETH Zurich, CH
,
Silvia Santini
TU Darmstadt, DE
,
Program Chairs:
John F. Canny
UC Berkeley, US
,
Marc Langheinrich
Università della Svizzera italiana, CH
,
Jun Rekimoto
University of Tokyo, JP

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.

In-Cooperation

SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 September 2013

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Demonstration

Conference

UbiComp '13

Sponsor:

SIGMOBILE
University of Florida
SIGCHI

UbiComp '13: The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 8 - 12, 2013

Zurich, Switzerland

Acceptance Rates

UbiComp '13 Adjunct Paper Acceptance Rate 254 of 399 submissions, 64%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

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Chen DShi XSong XChen ZZhang H(2023)Phone-based Ambient Temperature Measurement with a New Confidence-based Truth Inference ModelProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35703476:4(1-18)Online publication date: 11-Jan-2023
https://dl.acm.org/doi/10.1145/3570347
Khan ISun SRowe WThompson AAl-Hourani ASithamparanathan K(2022)Comparison of classifiers for use case detection using onboard smartphone sensors2022 32nd International Telecommunication Networks and Applications Conference (ITNAC)10.1109/ITNAC55475.2022.9998423(261-266)Online publication date: 30-Nov-2022
https://doi.org/10.1109/ITNAC55475.2022.9998423
Breda JTrivedi AWijesundara CBovornkeeratiroj PIrwin DShenoy PTaneja J(2019)Hot or NotProceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3360322.3360856(41-50)Online publication date: 13-Nov-2019
https://dl.acm.org/doi/10.1145/3360322.3360856
Du YIssarny VSailhan FLandsiedel ONahrstedt K(2019)User-centric context inference for mobile crowdsensingProceedings of the International Conference on Internet of Things Design and Implementation10.1145/3302505.3310088(261-266)Online publication date: 15-Apr-2019
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Agarwal RChopra SChristophides VGeorgantas NIssarny V(2019)Detecting Mobile Crowdsensing Context in the Wild2019 20th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM.2019.00-60(170-175)Online publication date: Jun-2019
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Chen HLi FHei XWang Y(2018)CrowdXProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32870372:4(1-21)Online publication date: 27-Dec-2018
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Chen SPark WYang JWagner D(2018)Inferring Phone Location State2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)10.1109/WiMOB.2018.8589190(42-47)Online publication date: Oct-2018
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Ionescu VEnescu F(2018)The implementation of a software system for detecting a pattern in an ambient light sensor data stream2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)10.1109/ECAI.2018.8679035(1-4)Online publication date: Jun-2018
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Bayındır L(2017)A survey of people-centric sensing studies utilizing mobile phone sensorsJournal of Ambient Intelligence and Smart Environments10.3233/AIS-1704469:4(421-448)Online publication date: 1-Jan-2017
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