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Sensing Emotions

By Gregory Mone

Communications of the ACM, Vol. 58 No. 9, Pages 15-16

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During the last Super Bowl, Emotient, a San Diego, CA-based company, hosted an unusual party. Emotient recruited 30 volunteers to a local bar to watch the game, eat, and drink. As the annual spectacle progressed on two televisions, a camera attached to each flat screen monitored the viewers. Behind the scenes, the Emotient Analytics system identified and tracked each face within the camera's view, then determined the changing emotional state of each individual over time. Whether they were amused, ambivalent, or surprised, the system matched those reactions to what was happening on screen, determining which commercials seemed to bore the viewers and which ones they liked enough to share. "We were able to predict which advertisements were likely to go viral based on facial behavior," says lead scientist Marian Bartlett.

Both the Emotient technology and a similar system from Waltham, MA-based Affectiva are products of the burgeoning field of affective computing, in which researchers are developing systems that estimate the internal emotional state of an individual based on facial expressions, vocal inflections, gestures, or other physiological cues. Affectiva and Emotient are catering to advertising and market research companies, but affective computing stands to impact a number of areas. The technique could lead to online learning systems that notice when a student is growing frustrated with a problem, healthcare applications that measure how a depressed patient is responding to a new medication, or in-home assistance robots that closely monitor the emotional state of the elderly.


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