Inferring user activities from spatial-temporal data in mobile phones

Gunarto Sindoro Njoo, Xiao Wen Ruan, Kuo Wei Hsu, Wen-Chih Peng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Activity inference is a key to the development of various ubiquitous computing applications. Here, we observe that users perform several actions in their mobile phone: take photos, perform check-in, and access Wi-Fi networks. These behaviors generate spatial-temporal data that could be utilized to capture user activities. Hence, three features are extracted for activities inference: 1) geographical feature: indicating where user performs activities; 2) temporal feature: indicating when user performs activities; and 3) semantic feature: showing semantic concept of a place from location-based social networks. Here, we propose Spatial-Temporal Activity Inference Model (STAIM) to infer users' activities from aforementioned features. Experimental results show that STAIM is able to effectively infer users' activities, achieving 75% accuracy on average.

Original languageEnglish
Title of host publicationUbiComp and ISWC 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers
PublisherAssociation for Computing Machinery, Inc
Pages65-68
Number of pages4
ISBN (Electronic)9781450335751
DOIs
StatePublished - 7 Sep 2015
EventACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2015 ACM International Symposium on Wearable Computers, UbiComp and ISWC 2015 - Osaka, Japan
Duration: 7 Sep 201511 Sep 2015

Publication series

NameUbiComp and ISWC 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers

Conference

ConferenceACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2015 ACM International Symposium on Wearable Computers, UbiComp and ISWC 2015
CountryJapan
CityOsaka
Period7/09/1511/09/15

Keywords

  • Activity inference
  • Classification
  • Spatial-temporal

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