Bayesian Modelling of the Temporal Aspects of Smart Home Activity with Circular Statistics

  • Tom DietheEmail author
  • Niall Twomey
  • Peter Flach
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9285)


Typically, when analysing patterns of activity in a smart home environment, the daily patterns of activity are either ignored completely or summarised into a high-level “hour-of-day” feature that is then combined with sensor activities. However, when summarising the temporal nature of an activity into a coarse feature such as this, not only is information lost after discretisation, but also the strength of the periodicity of the action is ignored. We propose to model the temporal nature of activities using circular statistics, and in particular by performing Bayesian inference with Wrapped Normal \(\mathcal {(WN)}\) and \(\mathcal {WN}\) Mixture \(\mathcal {(WNM)}\) models. We firstly demonstrate the accuracy of inference on toy data using both Gibbs sampling and Expectation Propagation (EP), and then show the results of the inference on publicly available smart-home data. Such models can be useful for analysis or prediction in their own right, or can be readily combined with larger models incorporating multiple modalities of sensor activity.


Mixture Model Bayesian Inference Bayesian Modelling Mixture Component Gibbs Sampling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Intelligent Systems LaboratoryUniversity of BristolBristolUK

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