A Framework for Human-centric Personalization of Context Recognition Models on Mobile Devices

In context recognition, the recognition model is often built by supervised machine-learning techniques. The recognition performance is likely to be influenced by individual difference, e.g., rhythm of walking, in the target domain. In this paper, we introduce a framework that allows mobile device users to be involved with customization process, in which the key concept is active learning and persuasive technologies. We show a case study with smartphone localization problem, in which we evaluated the effectiveness of active-learning supported by compatibility-based base classifier selection.