Health Monitoring in an Agent-Based Smart Home by Activity Prediction

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 dynamically and adaptively to detect and/or predict threatening long-term trends or immediate crises. The main objective of this paper is to investigate techniques for using agent-based smart home technologies to provide this athome health monitoring and assistance. To this end, we have developed novel prediction algorithms that will determine common activities of an inhabitant and report significant activity anomalies that may indicate health crises. Specifically, we address the following technological challenges: 1) developing secure situationor context-aware methods to collect at-home health and activity data, 2) learning patterns from the collected data, 3) identifying long-term trends of increasing or deteriorating health, 4) detecting and responding to anomalies in the data, and responding to predicted health problems, and 5) providing reminder and automation assistance to promote independent living at home. The proposed solution approaches are being tested in simulation and with volunteers at the UTA’s MavHome site, an agent-based smart home project funded by NSF.