Synthetic training data generation for activity monitoring and behavior analysis

Monekosso, Dorothy; Remagnino, Paolo

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. © Springer-Verlag Berlin Heidelberg 2009.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 4th International Workshop on Artificial Intelligence Techniques for Ambient Intelligence (AITAmI’09)
• Publication Date: Dec 1, 2009
• Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
• Print ISSN: 0302-9743
• Electronic ISSN: 1611-3349
• Publisher: Springer Verlag
• Peer Reviewed: Not Peer Reviewed
• Volume: 5859 LNCS
• Pages: 267-275
• DOI: https://doi.org/10.1007/978-3-642-05408-2_31
• Keywords: synthetic training data generation, activity monitoring, behavior analysis
• Public URL: https://uwe-repository.worktribe.com/output/990530
• Additional Information: Title of Conference or Conference Proceedings : 4th International Workshop on Artificial Intelligence Techniques for Ambient Intelligence (AITAmI’09)