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Despite much research targeted at enabling conventional machine learningmodels to continually learn tasks and data distributions sequentially withoutforgetting the knowledge acquired, little effort has been devoted to accountfor more realistic situations where learning some tasks accurately might bemore critical than forgetting previous ones. In this paper we propose aBayesian inference based framework to continually learn a set of real-world,sensing-based analysis tasks that can be tuned to prioritize the remembering ofpreviously learned tasks or the learning of new ones. Our experiments prove therobustness and reliability of the learned models to adapt to the changingsensing environment, and show the suitability of using uncertainty of thepredictions to assess their reliability.

Knowing when we do not know: Bayesian continual learning for sensing-based analysis tasks