Home to Home Transfer Learning

Activity recognition plays an important role in many areas such as smart environments by offering unprecedented opportunities for health monitoring, automation, security and energy efficiency. One challenge of activity recognition is the need for huge amounts of annotated data for each new physical setting. Our question is “how can we use activities learned in one space, e.g. home A, to recognize activities in another space, e.g home B?”. To answer this question, we propose a new method of transferring learned knowledge of activities to a new physical space in order to leverage the learning process in the new environment. Our method called ”Home to Home Transfer Learning” (HHTL) is based on using a semi EM framework and modeling activities using structural, temporal and spatial features. This method allows us to avoid the tedious task of collecting and labeling huge amounts of data in the target space, and allows for a more accelerated and more scalable deployment cycle in the real world. To validate our algorithms, we use the data collected in several smart apartments with different physical layouts.