A probabilistic model for cognitive-affective user state awareness

Author: Fernandez, S., Lazaro, I., Gilabert, E., Arnaiz, A., Munoz, F. M., & Castellanos, L. Date2013-07

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.

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