Learning latent activities from social signals with hierarchical dirichlet processes

Phung, D, Nguyen,T, Gupta, S and Venkatesh, S 2014, Learning latent activities from social signals with hierarchical dirichlet processes. In Sukthankar, Gita, Goldman, Robert P., Geib, Christopher, Pynadath, David V. and Bui, Hung Hai (ed), Plan, activity, and intent recognition : theory and practice, Elsevier, Boston, MA, pp.149-174, doi: 10.1016/B978-0-12-398532-3.00006-3.

Attached Files
Name Description MIMEType Size Downloads

Title Learning latent activities from social signals with hierarchical dirichlet processes
Author(s) Phung, DORCID iD for Phung, D orcid.org/0000-0002-9977-8247
Gupta, SORCID iD for Gupta, S orcid.org/0000-0002-3308-1930
Venkatesh, SORCID iD for Venkatesh, S orcid.org/0000-0001-8675-6631
Title of book Plan, activity, and intent recognition : theory and practice
Editor(s) Sukthankar, Gita
Goldman, Robert P.
Geib, Christopher
Pynadath, David V.
Bui, Hung Hai
Publication date 2014
Chapter number 6
Total chapters 14
Start page 149
End page 174
Total pages 26
Publisher Elsevier
Place of Publication Boston, MA
Keyword(s) activity recognition
bayesian nonparametric
healthcare monitoring
hierarchical dirichlet process
pervasive sensors
Summary Understanding human activities is an important research topic, most noticeably in assisted-living and healthcare monitoring environments. Beyond simple forms of activity (e.g., an RFID event of entering a building), learning latent activities that are more semantically interpretable, such as sitting at a desk, meeting with people, or gathering with friends, remains a challenging problem. Supervised learning has been the typical modeling choice in the past. However, this requires labeled training data, is unable to predict never-seen-before activity, and fails to adapt to the continuing growth of data over time. In this chapter, we explore the use of a Bayesian nonparametric method, in particular the hierarchical Dirichlet process, to infer latent activities from sensor data acquired in a pervasive setting. Our framework is unsupervised, requires no labeled data, and is able to discover new activities as data grows. We present experiments on extracting movement and interaction activities from sociometric badge signals and show how to use them for detecting of subcommunities. Using the popular Reality Mining dataset, we further demonstrate the extraction of colocation activities and use them to automatically infer the structure of social subgroups. © 2014 Elsevier Inc. All rights reserved.
ISBN 9780123985323
Language eng
DOI 10.1016/B978-0-12-398532-3.00006-3
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2014, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30067636

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 10 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 843 Abstract Views, 13 File Downloads  -  Detailed Statistics
Created: Mon, 24 Nov 2014, 12:02:03 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.