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Activity and device position recognition in mobile devices

Authors:

Sanjiv NandaAuthors Info & Claims

UbiComp '11: Proceedings of the 13th international conference on Ubiquitous computing

Pages 591 - 592

https://doi.org/10.1145/2030112.2030228

Published: 17 September 2011 Publication History

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Abstract

Activity recognition along with device position recognition can provide contextual cues suitable to infer user interruptibility and device accessibility. Our system fuses data from accelerometer and multiple light sensors to classify activities and device positions. Previously published results achieve robust activity recognition performance with multiple sensors attached to fixed body positions, a model suitable for use cases such as healthcare and fitness. We achieve comparable activity recognition performance using smartphones placed in unknown on-body positions including pocket, holster and hand. Results obtained from a diverse data set show that motion state and device position are classified with macro-averaged f-scores 92.6% and 66.8% respectively, over six activities and seven device positions. We demonstrate the performance of our classifier with an implementation running on the Android platform, that visitors can try out.

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Cited By

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Mekruksavanich SJitpattanakul A(2024)Device Position-Independent Human Activity Recognition with Wearable Sensors Using Deep Neural NetworksApplied Sciences10.3390/app1405210714:5(2107)Online publication date: 3-Mar-2024
https://doi.org/10.3390/app14052107
Aguileta ABrena RMayora OMolino-Minero-Re ETrejo L(2019)Multi-Sensor Fusion for Activity Recognition—A SurveySensors10.3390/s1917380819:17(3808)Online publication date: 3-Sep-2019
https://doi.org/10.3390/s19173808
Mairittha NInoue S(2019)Crowdsourcing System Management for Activity Data with Mobile Sensors2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR)10.1109/ICIEV.2019.8858566(85-90)Online publication date: May-2019
https://doi.org/10.1109/ICIEV.2019.8858566
Show More Cited By

Index Terms

Activity and device position recognition in mobile devices
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Intelligent agents

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Information & Contributors

Information

Published In

UbiComp '11: Proceedings of the 13th international conference on Ubiquitous computing

September 2011

668 pages

ISBN:9781450306300

DOI:10.1145/2030112

General Chairs:
James Landay
University of Washington, USA & Microsoft Research Asia, China
,
Yuanchun Shi
Tsinghua University, China
,
Program Chairs:
Donald J. Patterson
University of California, Irvine, USA
,
Yvonne Rogers
Open University, UK
,
Xing Xie
Microsoft Research Asia, China

In-Cooperation

University of Florida: University of Florida
SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 September 2011

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Ubicomp '11

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Ubicomp '11: The 2011 ACM Conference on Ubiquitous Computing

September 17 - 21, 2011

Beijing, China

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

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Cited By

View all

Mekruksavanich SJitpattanakul A(2024)Device Position-Independent Human Activity Recognition with Wearable Sensors Using Deep Neural NetworksApplied Sciences10.3390/app1405210714:5(2107)Online publication date: 3-Mar-2024
https://doi.org/10.3390/app14052107
Aguileta ABrena RMayora OMolino-Minero-Re ETrejo L(2019)Multi-Sensor Fusion for Activity Recognition—A SurveySensors10.3390/s1917380819:17(3808)Online publication date: 3-Sep-2019
https://doi.org/10.3390/s19173808
Mairittha NInoue S(2019)Crowdsourcing System Management for Activity Data with Mobile Sensors2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR)10.1109/ICIEV.2019.8858566(85-90)Online publication date: May-2019
https://doi.org/10.1109/ICIEV.2019.8858566
Elhoushi MGeorgy JNoureldin AKorenberg M(2017)A Survey on Approaches of Motion Mode Recognition Using SensorsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2016.261720018:7(1662-1686)Online publication date: Jul-2017
https://doi.org/10.1109/TITS.2016.2617200
Yang RWang B(2016)PACP: A Position-Independent Activity Recognition Method Using Smartphone SensorsInformation10.3390/info70400727:4(72)Online publication date: 15-Dec-2016
https://doi.org/10.3390/info7040072
Wu XMiucic RYang SAl-Stouhi SMisener JBai SChan W(2014)Cars Talk to Phones: A DSRC Based Vehicle-Pedestrian Safety System2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall)10.1109/VTCFall.2014.6965898(1-7)Online publication date: Sep-2014
https://doi.org/10.1109/VTCFall.2014.6965898
Kunze KLukowicz P(2014)Sensor Placement Variations in Wearable Activity RecognitionIEEE Pervasive Computing10.1109/MPRV.2014.7313:4(32-41)Online publication date: Oct-2014
https://doi.org/10.1109/MPRV.2014.73
Fan LWang ZWang H(2013)Human Activity Recognition Model Based on Decision TreeProceedings of the 2013 International Conference on Advanced Cloud and Big Data10.1109/CBD.2013.19(64-68)Online publication date: 13-Dec-2013
https://dl.acm.org/doi/10.1109/CBD.2013.19

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