Abstract
This chapter introduces an ontology-based hybrid approach to activity modeling that combines domain knowledge-based model specification and data-driven model learning. Central to the approach is an iterative process that begins with “seed” activity models created by ontological engineering. The “seed” models are then put in use, and subsequently evolved through incremental activity discovery and model update. While our previous work has detailed ontological activity modeling and activity recognition, this chapter focuses on the systematic hybrid approach and associated methods and inference rules for learning new activities and user activity profiles. An example case study has been used to demonstrate and evaluate the activity learning algorithms and mechanisms through which well-designed experiments in a feature-rich assistive living system.
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Chen, L., Nugent, C.D. (2019). A Hybrid Approach to Activity Modelling. In: Human Activity Recognition and Behaviour Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-19408-6_4
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DOI: https://doi.org/10.1007/978-3-030-19408-6_4
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