Learning latent activities from social signals with hierarchical Dirichlet processes

Dinh Phung, Thuong Nguyen, Sunil Gupta, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationPlan, Activity, and Intent Recognition
Subtitle of host publicationTheory and Practice
EditorsGita Sukthankar , Robert P. Goldman , Christopher Geib, David V. Pynadath, Hung Hai Bui
Place of PublicationWaltham MA USA
Number of pages26
ISBN (Print)9780123985323
Publication statusPublished - 2014
Externally publishedYes


  • Activity recognition
  • Bayesian nonparametric
  • Healthcare monitoring
  • Hierarchical dirichlet process
  • Pervasive sensors

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