In this paper we propose an ontology based representation of the affective states for context aware applications that allows expressing the complex relations that are among the affective states and between these and the other context elements. This representation is open to map different affective spaces; basic and secondary states relation (using Fuzzy Logic), the relation between these states and other context elements as location, time, person, activity etc. The proposed affective context model is encoded in OWL. Due to difficulties in direct detection of the secondary affective states we propose a method to infer the characteristic values of these states from other context elements' values. The deduces states are used here to improve the behavior of a Context Aware Museum Guide in order to react more intuitively and more intelligent by taking into account the user's affective states.

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Abstract-Human centred services are increasingly common in the market of mobile devices. However, affective aware services are still scarce. In turn, the recognition of secondary emotions in mobility conditions is critical to develop affective aware mobile applications. The emerging field of Affective Computing offers a few solutions to this problem. We propose a method to deduce user's secondary emotions based on context and personal profile. In a realistic environment, we defined a set of emotions common to a museum visit. Then we developed a context aware museum guide mobile application. To deduce affective states, we first used a method based on the user profile solely. Enhancement of this method with machine learning substantially improved the recognition of affective states. Implications for future work are discussed.

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