Tracking Systems for Multiple Smart Home Residents

Abstract

Once a smart home system moves to a multi-resident situation, it becomes significantly more important that individuals are tracked in some manner. By tracking individuals the events received from the sensor platform can then be separated into different streams and acted on independently by other tools within the smart home system. This process improves activity detection, history building and personalized interaction with the intelligent space. Historically, tracking has been primarily approached through a carried wireless device or an imaging system, such as video cameras. These are complicated approaches and still do not always effectively address the problem. Additionally, both of these solutions pose social problems to implement in private homes over long periods of time. This paper introduces and explores a Bayesian Updating method of tracking individuals through the space that leverages the Center for Advanced Studies in Adaptive Systems (CASAS) technology platform of pervasive and passive sensors. This approach does not require the residents to maintain a wireless device, nor does it incorporate rich sensors with the social privacy issues.