Un outil d’évaluation neurocognitive des interactions humain-machine
More and more researches on Human-Computer Interactions (HCI) are trying to perform detailed analyses of interaction to determine its influence on users’ behaviours. A particular emphasis is now put on emotional reactions during the interaction, whether it’s from the perspective of user experience evaluation or user performance. Standard qualitative approaches are limited because they are based on observations and interviews after the interaction, therefore limiting the precision of the diagnosis. User experience and emotional reactions being, by nature, highly dynamic and contextualized, evaluation approaches should be the same to accurately diagnose the quality of interaction. This thesis presents an evaluation approach, both dynamic and quantitative, which allows contextualising users’ emotional reactions to help identify their causes during the interaction with a system. To this end, our work focuses on three main axes: 1) automatic task recognition using machine learning modeling of eye tracking and interaction data; 2) automatic inference of psychological constructs (emotional activation, emotional valence, and cognitive load) through physiological signals analysis; and 3) diagnosis of users’ reactions during interaction based on the coupling of the two previous operations. The ideas and development of our approach are illustrated using two experimental contexts: e-commerce and simulation-based training. We also present the tool we implemented in order to allow HCI professionals (e.g.: user experience expert, training supervisor, or game designer) to use our evaluation approach to assess interaction. This tool is designed to facilitate the triangulation of measuring instruments and the integration with more classical Human-Computer Interaction methods (ex.: surveys and observation coding).
Human-computer interaction eye-tracking physiological measures task recognition user experience