research-article

Human activity recognition using social media data

Authors:

Alberto Calatroni,

Gerhard TrösterAuthors Info & Claims

MUM '13: Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia

Article No.: 21, Pages 1 - 10

https://doi.org/10.1145/2541831.2541852

Published: 02 December 2013 Publication History

Abstract

Human activity recognition is a core component of context-aware, ubiquitous computing systems. Traditionally, this task is accomplished by analyzing signals of wearable motion sensors. While such signals can effectively distinguish various low-level activities (e.g. walking or standing), two issues exist: First, high-level activities (e.g. watching movies or attending lectures) are difficult to distinguish from motion data alone. Second, instrumentation of complex body sensor network at population scale is impractical. In this work, we take an alternative approach of leveraging rich, dynamic, and crowd-generated self-report data as the basis for in-situ activity recognition. By treating the user as the "sensor", we make use of implicit signals emitted from natural use of mobile smart-phones. Applying an L1-regularized Linear SVM on features derived from textual content, semantic location, and time, we are able to infer 10 meaningful classes of daily life activities with a mean accuracy of up to 83.9%. Our work illustrates a promising first step towards comprehensive, high-level activity recognition using free, crowd-generated, social media data.

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Cited By

Zahtila MKnura M(2022)Visualizing Point Density on Geometry Objects: Application in an Urban Area Using Social Media VGIVisualisierung der Punktdichte auf Geometrieobjekten: Eine Anwendung von Social Media VGI im urbanen RaumKN - Journal of Cartography and Geographic Information10.1007/s42489-022-00113-772:3(187-200)Online publication date: 28-Jun-2022
https://doi.org/10.1007/s42489-022-00113-7
Knura MKluger FZahtila MSchiewe JRosenhahn BBurghardt D(2021)Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of DresdenISPRS International Journal of Geo-Information10.3390/ijgi1011073310:11(733)Online publication date: 28-Oct-2021
https://doi.org/10.3390/ijgi10110733
Jacucci GDaee PVuong TAndolina SKlouche KSjÖberg MRuotsalo TKaski S(2021)Entity Recommendation for Everyday Digital TasksACM Transactions on Computer-Human Interaction10.1145/345891928:5(1-41)Online publication date: 31-Oct-2021
https://dl.acm.org/doi/10.1145/3458919
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Index Terms

Human activity recognition using social media data
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Other conferences

MUM '13: Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia

December 2013

333 pages

ISBN:9781450326483

DOI:10.1145/2541831

General Chairs:
Matthias Kranz
Universität Passau, Germany
,
Kåre Synnes
Luleå University of Technology, Sweden
,
Program Chairs:
Sebastian Boring
University of Copenhagen, Denmark
,
Kristof Van Laerhoven
TU Darmstadt, Germany

Copyright © 2013 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 the author(s) 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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Luleå Univ. of Techn.: Luleå University of Technology

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 December 2013

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Hasler Stiftung

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MUM '13

Sponsor:

Luleå Univ. of Techn.

MUM '13: 12th International Conference on Mobile and Ubiquitous Multimedia

December 2 - 5, 2013

Luleå, Sweden

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MUM '13 Paper Acceptance Rate 36 of 107 submissions, 34%;

Overall Acceptance Rate 190 of 465 submissions, 41%

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Cited By

Zahtila MKnura M(2022)Visualizing Point Density on Geometry Objects: Application in an Urban Area Using Social Media VGIVisualisierung der Punktdichte auf Geometrieobjekten: Eine Anwendung von Social Media VGI im urbanen RaumKN - Journal of Cartography and Geographic Information10.1007/s42489-022-00113-772:3(187-200)Online publication date: 28-Jun-2022
https://doi.org/10.1007/s42489-022-00113-7
Knura MKluger FZahtila MSchiewe JRosenhahn BBurghardt D(2021)Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of DresdenISPRS International Journal of Geo-Information10.3390/ijgi1011073310:11(733)Online publication date: 28-Oct-2021
https://doi.org/10.3390/ijgi10110733
Jacucci GDaee PVuong TAndolina SKlouche KSjÖberg MRuotsalo TKaski S(2021)Entity Recommendation for Everyday Digital TasksACM Transactions on Computer-Human Interaction10.1145/345891928:5(1-41)Online publication date: 31-Oct-2021
https://dl.acm.org/doi/10.1145/3458919
Kim DHan SSon HLee D(2020)Human Activity Recognition Using Semi-supervised Multi-modal DEC for Instagram DataAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-47426-3_67(869-880)Online publication date: 6-May-2020
https://doi.org/10.1007/978-3-030-47426-3_67
Dmitry Namiot Manfred Sneps-Sneppe (2019)On Bikes in Smart CitiesAutomatic Control and Computer Sciences10.3103/S014641161901008553:1(63-71)Online publication date: 16-Apr-2019
https://doi.org/10.3103/S0146411619010085
Jussila JMadhala P(2019)Cognitive Computing Approaches for Human Activity Recognition from Tweets—A Case Study of Twitter Marketing CampaignResearch & Innovation Forum 201910.1007/978-3-030-30809-4_15(153-170)Online publication date: 29-Oct-2019
https://doi.org/10.1007/978-3-030-30809-4_15
Mohammad YMatsumoto KHoashi KHaddad HWainwright RChbeir R(2018)Deep feature learning and selection for activity recognitionProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3167234(930-939)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3167132.3167234
Mohammad YMatsumoto KHoashi K(2018)Primitive activity recognition from short sequences of sensory dataApplied Intelligence10.1007/s10489-018-1166-648:10(3748-3761)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s10489-018-1166-6
Vuong TJacucci GRuotsalo T(2017)Watching inside the ScreenProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/31309741:3(1-23)Online publication date: 11-Sep-2017
https://dl.acm.org/doi/10.1145/3130974
Kawaguchi NNishio NRoggen DInoue SPirttikangas SVan Laerhoven KLee STakayama LTruong K(2017)5th Int. workshop on human activity sensing corpus and applications (HASCA)Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers10.1145/3123024.3124410(530-536)Online publication date: 11-Sep-2017
https://dl.acm.org/doi/10.1145/3123024.3124410
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