Abstract
Although activity recognition is an active area of research no common benchmark for evaluating the performance of activity recognition methods exists. In this chapter we present the state of the art probabilistic models used in activity recognition and show their performance on several real world datasets. Our results can be used as a baseline for comparing the performance of other pattern recognition methods (both probabilistic and non-probabilistic). The datasets used in this chapter are made public, together with the source code of the probabilistic models used.
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van Kasteren, T.L.M., Englebienne, G., Kröse, B.J.A. (2011). Human Activity Recognition from Wireless Sensor Network Data: Benchmark and Software. In: Chen, L., Nugent, C., Biswas, J., Hoey, J. (eds) Activity Recognition in Pervasive Intelligent Environments. Atlantis Ambient and Pervasive Intelligence, vol 4. Atlantis Press. https://doi.org/10.2991/978-94-91216-05-3_8
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DOI: https://doi.org/10.2991/978-94-91216-05-3_8
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