Transfer learning for activity pattern in smart environment

Due to huge costs and operational difficulties in learning and mining activity patterns in smart environment, we propose a framework for activity pattern transfer. The process of activity pattern transfer is divided into two parts: trajectory transfer and trigger duration transfer. Firstly, fuzzify the trajectories and trigger durations in the existing activity patterns. Secondly, generate alternative trajectory set through alternative path set generation (ATSG) algorithm in a new environment. Finally, map the trajectories from activity patterns and trajectories from alternative trajectory set through similarity computing (SC) algorithm, and transfer activities' information through trajectory mapping (TM) algorithm and trigger duration transfer (TDT) algorithm. Thus, activity patterns are transferred to the new environment. Theoretical analysis and experimental results show that, compared to frequent pattern mining, the method in our paper can substantially reduce time overhead of obtaining activity patterns in new environment. At the same time, it performs well in recognizing activities.