Bayesian approach to sensor-based context awareness

  • Authors:

  • Panu Korpipää;Miika Koskinen;Johannes Peltola;Satu-Marja Mäkelä;Tapio Seppänen

  • Affiliations:

  • VTT Technical Research Centre of Finland, VTT Electronics, Kaitoväylä 1, P.O. Box 1100, FIN-90571, Oulu, Finland;VTT Technical Research Centre of Finland, VTT Electronics, Kaitoväylä 1, P.O. Box 1100, FIN-90571, Oulu, Finland;VTT Technical Research Centre of Finland, VTT Electronics, Kaitoväylä 1, P.O. Box 1100, FIN-90571, Oulu, Finland;VTT Technical Research Centre of Finland, VTT Electronics, Kaitoväylä 1, P.O. Box 1100, FIN-90571, Oulu, Finland;Department of Electrical and Information Engineering, University of Oulu, Oulu, Finland

  • Venue:
  • Personal and Ubiquitous Computing
  • Year:
  • 2003

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Abstract

AbstractThe usability of a mobile device and services can be enhanced by context awareness. The aim of this experiment was to expand the set of generally recognizable constituents of context concerning personal mobile device usage. Naive Bayesian networks were applied to classify the contexts of a mobile device user in her normal daily activities. The distinguishing feature of this experiment in comparison to earlier context recognition research is the use of a naive Bayes framework, and an extensive set of audio features derived partly from the algorithms of the upcoming MPEG-7 standard. The classification was based mainly on audio features measured in a home scenario. The classification results indicate that with a resolution of one second in segments of 5–30 seconds, situations can be extracted fairly well, but most of the contexts are likely to be valid only in a restricted scenario. Naive Bayes framework is feasible for context recognition. In real world conditions, the recognition accuracy using leave-one-out cross validation was 87% of true positives and 95% of true negatives, averaged over nine eight-minute scenarios containing 17 segments of different lengths and nine different contexts. Respectively, the reference accuracies measured by testing with training data were 88% and 95%, suggesting that the model was capable of covering the variability introduced in the data on purpose. Reference recognition accuracy in controlled conditions was 96% and 100%, respectively. However, from the applicability viewpoint, generalization remains a problem, as from a wider perspective almost any feature may refer to many possible real world situations.