@inproceedings{b832b5faa0e44bcc9ed2c1052870dc2f,
title = "Hierarchical activity recognition using automatically clustered actions",
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.",
keywords = "Activity recognition, Hierarchical models, Sensor networks, n/a OA procedure",
author = "\{van Kasteren\}, \{Tim L.M.\} and Gwenn Englebienne and Kr{\"o}se, \{Ben J.A.\}",
year = "2011",
doi = "10.1007/978-3-642-25167-2\_9",
language = "English",
isbn = "978-3-642-25166-5",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "82--91",
editor = "Keyson, \{David V.\} and Maher, \{Mary Lou\} and Norbert Streitz",
booktitle = "Ambient Intelligence",
address = "Germany",
note = "2nd International Joint Conference on Ambient Intelligence, AmI 2011 ; Conference date: 16-11-2011 Through 18-11-2011",
}