The term smart home refers to a living environment that by its connected sensors and actuators is capable of providing intelligent and contextualised support to its user. This may result in automated behaviors that blends into the user’s daily life. However, currently most smart homes do not provide such intelligent support. A first step towards such intelligent capabilities lies in learning automation rules by observing the user’s behavior. We present a new type of corpus for learning such rules from user behavior as observed from the events in a smart homes sensor and actuator network. The data contains information about intended tasks by the users and synchronized events from this network. It is derived from interactions of 59 users with the smart home in order to solve five tasks. The corpus contains recordings of more than 40 different types of data streams and has been segmented and pre-processed to increase signal quality. Overall, the data shows a high noise level on specific data types that can be filtered out by a simple smoothing approach. The resulting data provides insights into event patterns resulting from task specific user behavior and thus constitutes a basis for machine learning approaches to learn automation rules.
@InProceedings{ENGELMANN16.1014,
author = {Kai Frederic Engelmann and Patrick Holthaus and Britta Wrede and Sebastian Wrede}, title = {An Interaction-Centric Dataset for Learning Automation Rules in Smart Homes}, booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)}, year = {2016}, month = {may}, date = {23-28}, location = {Portorož, Slovenia}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {978-2-9517408-9-1}, language = {english} }
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