Spatial activity recognition in a smart home environment using a chemotactic model
Riedel, Daniel E., Venkatesh, Svetha and Liu, Wanquan 2005, Spatial activity recognition in a smart home environment using a chemotactic model, in Proceedings of the 2005 intelligent sensors, sensor networks and information processing conference, IEEE, Piscataway, N.J., pp. 301-306, doi: 10.1109/ISSNIP.2005.1595596.
Spatial activity recognition is challenging due to the amount of noise incorporated during video tracking in everyday environments. We address the spatial recognition problem with a biologically-inspired chemotactic model that is capable of handling noisy data. The model is based on bacterial chemotaxis, a process that allows bacteria to change motile behaviour in relation to environmental gradients. Through adoption of chemotactic principles, we propose the chemotactic model and evaluate its performance in a smart house environment. The model exhibits greater than 99% recognition performance with a diverse six class dataset and outperforms the Hidden Markov Model (HMM). The approach also maintains high accuracy (90-99%) with small training sets of one to five sequences. Importantly, unlike other low-level spatial activity recognition models, we show that the chemotactic model is capable of recognising simple interwoven activities.
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