Ambiance Signal Processing: A Study on Collaborative Affective Computing

Computational feature recognition is an essential component for intelligent systems to sense the objects and environments. This paper proposes a novel conceptual model, named Ambiance Signal Processing (AmSiP), to identify objects' features when they are not directly accessible by sensors. AmSiP analyzes the surrounding and ambiance of objects/subjects collaboratively to recognize the object's features instead of concentrating on each individual and accessible object. To validate the proposed model, this study runs an experiment with 50 participants, whose emotional state variations are estimated by measuring the surroundings features and the emotions of other people in the same environment. The results of a t-Test on the data collected from this experiment showed that users' emotions were being changed in a course of time during the experiment; however, AmSiP could estimate subjects' emotions reliably according to the environmental characteristics and similar patterns. To evaluate the reliability and efficiency of this model, a collaborative affective computing system was implemented using keyboard keystroke dynamics and mouse interactions of the users whose emotions were affected by different types of music. In comparison with other conventional techniques (explicit access), the prediction was reliable. Although the developed model sacrifices a minor accuracy, it earns the superiority of uncovering blind knowledge about the subjects out of the sensors' direct access.