Fault detection for binary sensors in smart home environments
Abstract
Experiments in assisted living confirm that such systems can provide context-aware services that enable occupants to remain active and independent. They also demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a technique that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets that include multiple individuals, and show consistent rates of anomaly detection across different environments.
Citation
Ye , J , Stevenson , G & Dobson , S 2015 , Fault detection for binary sensors in smart home environments . in 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom) . IEEE Computer Society , pp. 20-28 , IEEE International Conference on Pervasive Computing and Communications (PerCom 2015) , St Louis, Missouri , United States , 23/03/15 . https://doi.org/10.1109/PERCOM.2015.7146505 conference
Publication
2015 IEEE International Conference on Pervasive Computing and Communications (PerCom)
Type
Conference item
Rights
© 2015. IEEE. This is the accepted mansucript of a conference paper originally submitted to the IEEE International Conference on Pervasive Computing and Communications, Fault detection for binary sensors in smart home environments Ye, J., Stevenson, G. & Dobson, S. 23 Mar 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom 2015). IEEE Computer Society, available from http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000551
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