The Independent Lifestyle Assistant (I.L.S.A.) is agent-based system to aid elderly people to live longer in their homes. One of the biggest challenges for I.L.S.A. is that every installation will (1) be in a home with a different layout and suite of sensor and actuator capabilities, and (2) supporting a technophobic client with unique capabilities, needs and care-giving support network. I.L.S.A. therefore must be able to adapt to its environment over time. This paper describes our approach to modelling one particular aspect of the I.L.S.A. domain: using sequential pattern learning to learn models of human behaviour. We describe the problem, our enhancements to the basic algorithm, and present experimental results collected from four test sites.