A problem in performing activity recognition on a large scale (i.e. in many homes) is that a labelled data set needs to be recorded for each house activity recognition is performed in. This is because most models for activity recognition require labelled data to learn their parameters. In this paper we introduce a transfer learning method for activity recognition which allows the use of existing labelled data sets of various homes to learn the parameters of a model applied in a new home. We evaluate our method using three large real world data sets and show our approach achieves good classification performance in a home for which little or no labelled data is available. © 2010 Springer-Verlag.
CITATION STYLE
Van Kasteren, T. L. M., Englebienne, G., & Kröse, B. J. A. (2010). Transferring knowledge of activity recognition across sensor networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6030 LNCS, pp. 283–300). https://doi.org/10.1007/978-3-642-12654-3_17
Mendeley helps you to discover research relevant for your work.