Detecting user dissatisfaction in ambient intelligence environments

One of the reasons that explains the slow uptake of smart home technologies has been addressed several times due to a lack of designs better aware of usability and the real context when families experience their daily life. The present paper introduces a system model for ambient intelligence (AmI) environments that boosts a friendly system-user´s adaptation. To that end, system self-checking capabilities based on the detection of levels of user dissatisfaction or disagreement are developed.