Affective computing involves the study and development of systems and devices that can recognize and analyze the expression of human emotions (affects). Such systems often rely on statistical models created from data collected by monitoring users and their affective response to various stimuli. A user's affective response to stimuli is typically a temporary departure from a steady baseline affective state. The baseline state is the state the user would typically be in when not exposed to any particular stimuli. In order to accurately model and understand the effects of various stimuli on the user, the user's affective response needs to be evaluated relative to the user's affective baseline value; otherwise, it is difficult to determine for instance, if a change in the user's affective state is due the stimuli, or is simply a natural return to the user's baseline affective state.
One common method to compute a baseline value is by measuring the user in a rested state before the exposure to stimuli (for example before an experiment starts). Another common method to compute a baseline is by weighting and processing the user's affective response values collected in a relatively wide window, for example by taking the average of values collected over a one hour. The problem with the former method for computing a user's baseline is that it is usually only applicable in controlled situations, for example, experiments in laboratories. In real world scenarios, it is likely the user will have neither the willingness nor the opportunity to provide an accurate baseline measurement from a calm period, before each exposure to stimuli. The later method described for computing baselines has a weakness, which can often make the baseline values computed by this method very inaccurate. This method does not take into account the situation the user is in. In real world scenario, at different times, the user may be in very different situations and the user's baseline value can change dramatically depending on the situation. For example, a user's affective baseline while working in the office might be quite different from the user's baseline when relaxing at home. In addition, the user's affective baseline can change dramatically in a very short when the situation changes. For example, a user's affective baseline state can change within seconds from something that can be described as calm and relaxed, for instance while cruising down highway 101, to tense and agitated when the user notices a police car in the rear-view mirror (along with the realization that the user was driving at a speed of 85 mph). Since the baseline value computed with a wide window changes slowly, it cannot adjust to a user's situation dependent affective baseline. This may make any models or predictions of the user's response to stimuli inaccurate.