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

Authors:
Liang Wang;Tao Gu;Xianping Tao;Hanhua Chen;Jian Lu
Affiliations:
State Key Laboratory for Novel Software Technology, Nanjing University, China and Department of Mathematics and Computer Science, University of Southern Denmark, Denmark;Department of Mathematics and Computer Science, University of Southern Denmark, Denmark;State Key Laboratory for Novel Software Technology, Nanjing University, China;School of Computer Science and Technology, Huazhong University of Science and Technology, China and Department of Mathematics and Computer Science, University of Southern Denmark, Denmark;State Key Laboratory for Novel Software Technology, Nanjing University, China
Venue:
Pervasive and Mobile Computing
Year:
2011

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Abstract

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.