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Understanding and recognizing human activities from sensor readings is an important task in pervasive computing. Existing work on activity recognition mainly focuses on recognizing activities for a single user in a smart home environment. However, in real life, there are often multiple inhabitants live in such an environment. Recognizing activities of not only a single user, but also multiple users is essential to the development of practical context-aware applications in pervasive computing. In this paper, we investigate the fundamental problem of recognizing activities for multiple users from sensor readings in a home environment, and propose a novel pattern mining approach to recognize both single-user and multi-user activities in a unified solution. We exploit Emerging Patterna type of knowledge pattern that describes significant changes between classes of data -for constructing our activity models, and propose an Emerging Pattern based Multi-user Activity Recognizer (epMAR) to recognize both single-user and multiuser activities. We conduct our empirical studies by collecting real-world activity traces done by two volunteers over a period of two weeks in a smart home environment, and analyze the performance in detail with respect to various activity cases in a multi-user scenario. Our experimental results demonstrate that our epMAR recognizer achieves an average accuracy of 89.72% for all the activity cases.

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