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An unsupervised learning method for human activity recognition based on a temporal qualitative model

Abstract : In this paper, we investigate the problem of monitoring human activities using a network of sensors, including video cameras, in a smart home environment. We introduce an unsupervised method for mining a new kind of qualitative temporally structured activity models from sensor data. We present an application of our method to the recognition of activities of daily living in an elderly care context.
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https://hal.inria.fr/inria-00624367
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Submitted on : Friday, September 16, 2011 - 4:28:00 PM
Last modification on : Thursday, November 25, 2021 - 8:22:28 AM
Long-term archiving on: : Saturday, December 17, 2011 - 2:26:41 AM

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  • HAL Id : inria-00624367, version 1

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Franck Vandewiele, Cina Motamed. An unsupervised learning method for human activity recognition based on a temporal qualitative model. International Workshop on Behaviour Analysis and Video Understanding (ICVS 2011), Sep 2011, Sophia Antipolis, France. pp.9. ⟨inria-00624367⟩

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