The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Designing a Home of the Future
IEEE Pervasive Computing
The smart floor: a mechanism for natural user identification and tracking
CHI '00 Extended Abstracts on Human Factors in Computing Systems
Multi-Camera Multi-Person Tracking for EasyLiving
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
MOBIQUITOUS '05 Proceedings of the The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services
How smart are our environments? An updated look at the state of the art
Pervasive and Mobile Computing
Multi-modal emotive computing in a smart house environment
Pervasive and Mobile Computing
Online Sequential Prediction via Incremental Parsing: The Active LeZi Algorithm
IEEE Intelligent Systems
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Findings from a participatory evaluation of a smart home application for older adults
Technology and Health Care
Activity recognition via user-trace segmentation
ACM Transactions on Sensor Networks (TOSN)
Inhabitant guidance of smart environments
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: interaction platforms and techniques
Toward scalable activity recognition for sensor networks
LoCA'06 Proceedings of the Second international conference on Location- and Context-Awareness
Video security for ambient intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
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Smart environment research has resulted in many useful tools for modeling, monitoring, and adapting to a single resident. However, many of these tools are not equipped for coping with multiple residents in the same environment simultaneously. In this paper we investigate a first step in coping with multiple residents, that of attributing sensor events to individuals in a multi-resident environment. We discuss approaches that can be used to achieve this goal and we evaluate our implementations in the context of two physical smart environment testbeds. We also explore how learning resident identifiers can aid in performing other analyses on smart environment sensor data such as activity recognition.