A sensor fusion approach for measuring emotional customer experience in an intelligent retail environment

Customer experience depends not only on the aspects which retailers can easily control, but also on emotional factors that are unpredictable. In this paper, a Multi-Task MultiKernel learning approach is proposed to recognise positive users' emotion in a retail scenario. The overall system is composed by the Ultra-Wide Band (UWB) tracking system and a consumer smartwatch device. Data gathered from sensors are combined in a multi-kernel scenario to estimate shoppers emotion (i.e., valence and arousal) which is strictly correlated to different shoppers feelings. Results in term of accuracy and macro-F1 score prove the effectiveness and the suitability of the proposed approach