Recognizing multi-user activities using wearable sensors in a smart home

Liang Wang, Tao Gu*, Xianping Tao, Hanhua Chen, Jian Lu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

123 Citations (Scopus)


The advances of wearable sensors and wireless networks offer many opportunities to recognize human activities from sensor readings in pervasive computing. Existing work so far focuses mainly on recognizing activities of a single user in a home environment. However, there are typically multiple inhabitants in a real home and they often perform activities together. In this paper, we investigate the problem of recognizing multi-user activities using wearable sensors in a home setting. We develop a multi-modal, wearable sensor platform to collect sensor data for multiple users, and study two temporal probabilistic models—Coupled Hidden Markov Model (CHMM) and Factorial Conditional Random Field (FCRF)—to model interacting processes in a sensor-based, multi-user scenario. We conduct a real-world trace collection done by two subjects over two weeks, and evaluate these two models through our experimental studies. Our experimental results show that we achieve an accuracy of 96.41% with CHMM and an accuracy of 87.93% with FCRF, respectively, for recognizing multi-user activities.
Original languageEnglish
Pages (from-to)287-298
Number of pages12
JournalPervasive and Mobile Computing
Issue number3
Publication statusPublished - Jun 2011
Externally publishedYes


  • Sensor-based human activity recognition
  • Multi-user activity recognition
  • Probabilistic model
  • Wireless sensor network


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