Synthetic Training Data Generation for Activity Monitoring and Behavior Analysis

  • Dorothy Monekosso
  • Paolo Remagnino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5859)


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.


Synthetic data generation perturbation model statistical analysis 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Dorothy Monekosso
    • 1
  • Paolo Remagnino
    • 2
  1. 1.CSRIUniversity of UlsterJordanstownUK
  2. 2.CISMKingston UniversityLondonUK

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