Annotation of multimedia data is usually done manually by human annotators or automatically, using algorithms that directly analyze the multimedia data. An alternative to these methods is the use of implicit tagging, where the aim is to observe the user as he/she engages with the multimedia data and infer knowledge about the data by analyzing the user's reactions. Implicit tagging can be performed through various modalities, such as facial expressions, body gestures, EEG analysis, gaze tracking, verbal and non-verbal communication etc.

In this talk we we focus on the continuum of human sensing in a sensor-equipped environment for 'off-line tagging' for future retrieval purposes and 'on-line tagging' for real-time support of a user. What can we learn from a user by observing his or her interaction patterns with an application or with another human? And how can we make off-line and on-line use of this knowledge? Sensor-equipped humans will become 'things' (or nodes) in the 'Internet of Things'. They will voluntarily and involuntarily feed the Internet of Things with their behavioral information, allowing the 'Internet of Things' to do behavioral retrieval, but also allowing the 'Internet of Things' to learn more about their life than they may find acceptable.

In our talk we will also look at possibilities to deceive the environment, that is, feeding the environment with signals that hide our true feelings and intentions. This is not unusual in daily life, and being able to do so in 'everywhere and always' sensing environments will make it possible to maintain 'naturalness' in our behavior in such environments.