Attributing events to individuals in multi-inhabitant environments

Intelligent environment research has resulted in many useful tools such as activity recognition, prediction, and automation. However, most of these techniques have been applied in the context of a single resident. A current looming issue for intelligent environment systems is performing these same techniques when multiple residents are present in the environment. In this paper we investigate the problem of attributing sensor events to individuals in a multi-resident intelligent environment. Specifically, we use a naive Bayesian classifier to identify the resident responsible for a unique sensor event. We present results of experimental validation in a real intelligent workplace testbed and discuss the unique issues that arise in addressing this challenging problem.