Abstract
The ability to recognize human activities from sensory information is essential for developing the next generation of smart devices. Many human activity recognition tasks are – from a machine learning perspective – quite similar to tagging tasks in natural language processing. Motivated by this similarity, we develop a relational transformation-based tagging system based on inductive logic programming principles, which is able to cope with expressive relational representations as well as a background theory. The approach is experimentally evaluated on two activity recognition tasks and an information extraction task, and compared to Hidden Markov Models, one of the most popular and successful approaches for tagging.