Automatic detection of the level of human interest is of high relevance for many technical applications, such as automatic customer care or tutoring systems. However, the recognition of spontaneous interest in natural conversations independently of the subject remains a challenge. Identification of human affective states relying on single modalities only is often impossible, even for humans, since different modalities contain partially disjunctive cues. Multimodal approaches to human affect recognition generally are shown to boost recognition performance, yet are evaluated in restrictive laboratory settings only. Herein we introduce a fully automatic processing combination of Active-Appearance-Model-based facial expression, vision-based eyeactivity estimation, acoustic features, linguistic analysis, non-linguistic vocalisations, and temporal context information in an early feature fusion process. We provide detailed subject-independent results for classification and regression of the Level of Interest using Support-Vector Machines on an audiovisual interest corpus (AV IC) consisting of spontaneous, conversational speech demonstrating "theoretical" effectiveness of the approach. Further, to evaluate the approach with regards to real-life usability a user-study is conducted for proof of "practical" effectiveness.

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We report on approaches for context-awareness in a kitchen environment. Two devices, an augmented cutting board and a sensor-enriched knife, enable the environment to determine the type of food handled during the preparation of meals.

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