Editing Affective computing (section)

=== Other applications ===

Affective computing has potential applications in [[human computer interaction|human–computer interaction]], such as affective mirrors allowing the user to see how he or she performs; emotion monitoring agents sending a warning before one sends an angry email; or even music players selecting tracks based on mood.<ref>{{cite journal|last1=Janssen|first1=Joris H.|last2=van den Broek|first2=Egon L.|date=July 2012|title=Tune in to Your Emotions: A Robust Personalized Affective Music Player|journal=User Modeling and User-Adapted Interaction|volume=22|issue=3|pages=255–279|doi=10.1007/s11257-011-9107-7|doi-access=free|hdl=2066/103051|hdl-access=free}}</ref>

One idea put forth by the Romanian researcher Dr. Nicu Sebe in an interview is the analysis of a person's face while they are using a certain product (he mentioned ice cream as an example).<ref>{{cite web|url=https://www.sciencedaily.com/videos/2006/0811-mona_lisa_smiling.htm|title=Mona Lisa: Smiling? Computer Scientists Develop Software That Evaluates Facial Expressions|date=1 August 2006|website=ScienceDaily|archive-url=https://web.archive.org/web/20071019235625/http://sciencedaily.com/videos/2006/0811-mona_lisa_smiling.htm|archive-date=19 October 2007|url-status=dead}}</ref> Companies would then be able to use such analysis to infer whether their product will or will not be well received by the respective market.

One could also use affective state recognition in order to judge the impact of a TV advertisement through a real-time video recording of that person and through the subsequent study of his or her facial expression. Averaging the results obtained on a large group of subjects, one can tell whether that commercial (or movie) has the desired effect and what the elements which interest the watcher most are.