Relational Transformation-based Tagging for Human Activity Recognition

author: Niels Landwehr, University of Freiburg
published: Jan. 29, 2008,   recorded: September 2007,   views: 3425

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


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 compared to Hidden Markov Models, one of the most popular and successful approaches for tagging.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: