A fusion-based approach for user activities recognition on smart phones

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

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

4 Scopus citations

Abstract

In the recent years, several research works have been conducted on collecting context data from various sensors for activity inference. We observe that users perform several actions in their mobile phones: taking photos, performing check-ins, and accessing Wi-Fi networks. These actions generate spatial-temporal data that could be utilized to capture user activities. Spatial-temporal data could indicate that a user stays in a certain location at a particular time for a certain activity. In addition, by referring to social media data, one could also infer user activities. Three types of features are extracted for activity inference: 1) geographical feature, indicating where a user performs activities; 2) temporal feature, indicating when a user performs activities; and 3) semantic feature, showing the semantic concept of a place from location-based social networks. Here, we propose Spatial-Temporal Activity Inference Model (STAIM) to infer user activities from data with those three features. In addition, to determine the weight for each feature, we further propose three methods based on frequency, entropy, and entropy-frequency. Experimental results show that STAIM is able to effectively infer user activities, achieving 75% accuracy on average. Moreover, STAIM could infer user activities even when there is no training data (with some performance loss). Moreover, sensitive analysis of parameters is also conducted to select the most optimal parameter.

Original languageEnglish
Title of host publicationProceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015
EditorsGabriella Pasi, James Kwok, Osmar Zaiane, Patrick Gallinari, Eric Gaussier, Longbing Cao
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467382731
DOIs
StatePublished - 2 Dec 2015
EventIEEE International Conference on Data Science and Advanced Analytics, DSAA 2015 - Paris, France
Duration: 19 Oct 201521 Oct 2015

Publication series

NameProceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015

Conference

ConferenceIEEE International Conference on Data Science and Advanced Analytics, DSAA 2015
CountryFrance
CityParis
Period19/10/1521/10/15

Fingerprint Dive into the research topics of 'A fusion-based approach for user activities recognition on smart phones'. Together they form a unique fingerprint.

Cite this