Abstract
This paper describes a data generator that produces synthetic data to simulate observations from an array of environment monitoring sensors. The overall goal of our work is to monitor the well-being of one occupant in a home. Sensors are embedded in a smart home to unobtrusively record environmental parameters. Based on the sensor observations, behavior analysis and modeling are performed. However behavior analysis and modeling require large data sets to be collected over long periods of time to achieve the level of accuracy expected. A data generator - was developed based on initial data i.e. data collected over periods lasting weeks to facilitate concurrent data collection and development of algorithms. The data generator is based on statistical inference techniques. Variation is introduced into the data using perturbation models.
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Monekosso, D., Remagnino, P. (2009). Synthetic Training Data Generation for Activity Monitoring and Behavior Analysis. In: Tscheligi, M., et al. Ambient Intelligence. AmI 2009. Lecture Notes in Computer Science, vol 5859. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05408-2_31
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DOI: https://doi.org/10.1007/978-3-642-05408-2_31
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