Hierarchical activity recognition using automatically clustered actions

Authors:
Tim L. M. van Kasteren;Gwenn Englebienne;Ben J. A. Kröse
Affiliations:
Department of Computer Engineering, Boğaziçi University, Istanbul, Turkey;Intelligent Systems Lab Amsterdam, University of Amsterdam, The Netherlands;Intelligent Systems Lab Amsterdam, University of Amsterdam, The Netherlands
Venue:
AmI'11 Proceedings of the Second international conference on Ambient Intelligence
Year:
2011

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Abstract

The automatic recognition of human activities such as cooking, showering and sleeping allows many potential applications in the area of ambient intelligence. In this paper we show that using a hierarchical structure to model the activities from sensor data can be very beneficial for the recognition performance of the model. We present a two-layer hierarchical model in which activities consist of a sequence of actions. During training, sensor data is automatically clustered into clusters of actions that best fit to the data, so that sensor data only has to be labeled with activities, not actions. Our proposed model is evaluated on three real world datasets and compared to two non-hierarchical temporal probabilistic models. The hierarchical model outperforms the non-hierarchical models in all datasets and does so significantly in two of the three datasets.