A probabilistic model for cognitive-affective user state awareness

In this work we describe a cognitive model to infer the more likely user's states in data-intensive contexts. Stress, mental fatigue, or even inaptitude, are selected to be inferred by the model based two sources of information: context and psycho-physiological sensors network. As long as a complex, high demanding context will predict those cognitive states that, in turn, will be diagnosed by the set of sensors (EEG and ECG). All these input variables are represented in a probabilistic model in which links are defined based on the literature. The outcome of the model is a probability of being inapt to perform in a suitable way. In case of inaptitude, assistance should be delivered to the user to normalize the current user's state.