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Learning latent activities from social signals with hierarchical dirichlet processes
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posted on 2014-03-01, 00:00 authored by Quoc-Dinh Phung, Cong Thuong Nguyen, Sunil GuptaSunil Gupta, Svetha VenkateshSvetha VenkateshUnderstanding 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.
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Title of book
Plan, activity, and intent recognition : theory and practiceChapter number
6Pagination
149 - 174Publisher
ElsevierPlace of publication
Boston, Mass.Publisher DOI
ISBN-13
9780123985323ISBN-10
0123985323Language
engPublication classification
B Book chapter; B1 Book chapterCopyright notice
2014, ElsevierExtent
14Editor/Contributor(s)
Sukthankar, Gita, Goldman, Robert P., Geib, Christopher, Pynadath, David V., Bui, Hung HaiUsage metrics
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