poster

Inferring social contextual behavior from bluetooth traces

Authors:

Xingyu Gao, and

Andrew T. CampbellAuthors Info & Claims

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

September 2013

Pages 267 - 270

https://doi.org/10.1145/2494091.2494176

Published: 08 September 2013 Publication History

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Abstract

Context-aware computing is increasingly paid much attention, especially makes the people's social contextual behavior very crucial for user-centric dynamic behavior inference. At present, extensive work has focused on detecting specific places inferred by static radio signals like GPS, GSM and WiFi, and recognizing mobility modes inferred by embedded sensor components like accelerometer. This paper proposes a distinct feature based classification approach and context restraint based majority vote rule to infer social contextual behavior in dynamic surroundings. Experimental results indicate that our proposed method can achieve high accuracy for inferring social contextual behavior through the real-life Bluetooth traces.

References

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Su ZPahlavan KAgu E(2021)Performance Evaluation of COVID-19 Proximity Detection Using Bluetooth LE SignalIEEE Access10.1109/ACCESS.2021.30643239(38891-38906)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3064323
Guo YSu LChen ZWen ZPang ZWang ZDu J(2021)The Detection Method of High Frequency Electromagnetic Field for the Malicious Use of Electric Energy MetersApplication of Intelligent Systems in Multi-modal Information Analytics10.1007/978-3-030-74814-2_74(522-528)Online publication date: 17-Apr-2021
https://doi.org/10.1007/978-3-030-74814-2_74
Wang ZGong YChen ZSun NWang WGuo YZhao F(2021)An Electromagnetic Field Detection Module of Preventing Stealing ElectricityApplication of Intelligent Systems in Multi-modal Information Analytics10.1007/978-3-030-74814-2_73(514-521)Online publication date: 17-Apr-2021
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Index Terms

Inferring social contextual behavior from bluetooth traces
1. Human-centered computing

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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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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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UBICOMP '24

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The 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing

October 5 - 9, 2024

Melbourne , VIC , Australia

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Cited By

View all

Su ZPahlavan KAgu E(2021)Performance Evaluation of COVID-19 Proximity Detection Using Bluetooth LE SignalIEEE Access10.1109/ACCESS.2021.30643239(38891-38906)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3064323
Guo YSu LChen ZWen ZPang ZWang ZDu J(2021)The Detection Method of High Frequency Electromagnetic Field for the Malicious Use of Electric Energy MetersApplication of Intelligent Systems in Multi-modal Information Analytics10.1007/978-3-030-74814-2_74(522-528)Online publication date: 17-Apr-2021
https://doi.org/10.1007/978-3-030-74814-2_74
Wang ZGong YChen ZSun NWang WGuo YZhao F(2021)An Electromagnetic Field Detection Module of Preventing Stealing ElectricityApplication of Intelligent Systems in Multi-modal Information Analytics10.1007/978-3-030-74814-2_73(514-521)Online publication date: 17-Apr-2021
https://doi.org/10.1007/978-3-030-74814-2_73
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Hernandez NDemiray BArnrich BFavela JMayora OForti SMeyer JMamykina L(2019)An Exploratory Study to Detect Temporal Orientation Using Bluetooth's sensorProceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare10.1145/3329189.3329223(292-297)Online publication date: 20-May-2019
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Kazagli EChen JBierlaire M(2018)Individual Mobility Analysis Using Smartphone DataIntelligent Transportation and Planning10.4018/978-1-5225-5210-9.ch015(332-354)Online publication date: 2018
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Suffoletto BScaglione S(2018)Using Digital Interventions to Support Individuals with Alcohol Use Disorder and Advanced Liver Disease: A Bridge Over Troubled WatersAlcoholism: Clinical and Experimental Research10.1111/acer.1377142:7(1160-1165)Online publication date: 13-Jun-2018
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Surrounding context and episode awareness using dynamic Bluetooth data

Contexto: lessons learned from mobile context inference

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