Extending knowledge-driven activity models through data-driven learning techniques
We combine knowledge- and data-driven approaches for activity modeling. We develop a novel clustering algorithm that uses prior domain expert knowledge. A new learning algorithm to model activities from extracted clusters. We model a pervasive home environment with real users inputs for experiments. Automatically learn 100% of activity variations performed by users.
knowledge-driven activity model learning activity recognition