Monitoring Health by Detecting Drifts and Outliers for a Smart Environment Inhabitant

To many people, home is a sanctuary. For those people who need special medical care, they may need to be pulled out of their home to meet their medical needs. As the population ages, the percentage of people in this group is increasing and the effects are expensive as well as unsatisfying. We hypothesize that many people with disabilities can lead independent lives in their own homes with the aid of at-home automated assistance and health monitoring. In order to accomplish this, robust methods must be developed to collect relevant data and process it to detect and/or predict threatening long-term trends or immediate crises. The main objective of this work is to design techniques for using agent-based smart home technologies to provide this at-home health monitoring and assistance. Specifically, we address the following technological challenges: 1) identifying lifestyle trends, 2) detecting anomalies in current data, and 3) designing a reminder assistance system. We discuss one such smart environment implementation in the MavHome project and present results from testing these techniques in simulation and with a volunteer in an apartment setting.