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CRAFFT: an activity prediction model based on Bayesian networks

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Abstract

Recent advances in the areas of pervasive computing, data mining, and machine learning offer unique opportunities to provide health monitoring and assistance for individuals facing difficulties to live independently in their homes. Several components have to work together to provide health monitoring for smart home residents including, but not limited to, activity recognition, activity discovery, activity prediction, and prompting system. Compared to the significant research done to discover and recognize activities, less attention has been given to predict the future activities that the resident is likely to perform. Activity prediction components can play a major role in design of a smart home. For instance, by taking advantage of an activity prediction module, a smart home can learn context-aware rules to prompt individuals to initiate important activities. In this paper, we propose an activity prediction model using Bayesian networks together with a novel two-step inference process to predict both the next activity features and the next activity label. We also propose an approach to predict the start time of the next activity which is based on modeling the relative start time of the predicted activity using the continuous normal distribution and outlier detection. To validate our proposed models, we used real data collected from physical smart environments.

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Notes

  1. Also referred to as the “explaining away” situation.

  2. Datasets are available online at http://casas.eecs.wsu.edu/datasets/.

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, Washington State University, EME 206 Spokane Street, Pullman, WA, USA

    Ehsan Nazerfard

  2. School of Electrical Engineering and Computer Science, Washington State University, EME 121 Spokane Street, Pullman, WA, USA

    Diane J. Cook

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  1. Ehsan Nazerfard
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  2. Diane J. Cook
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Correspondence to Ehsan Nazerfard.

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Nazerfard, E., Cook, D.J. CRAFFT: an activity prediction model based on Bayesian networks. J Ambient Intell Human Comput 6, 193–205 (2015). https://doi.org/10.1007/s12652-014-0219-x

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  • DOI: https://doi.org/10.1007/s12652-014-0219-x

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