Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
Affective computing
CT '01 Proceedings of the 4th International Conference on Cognitive Technology: Instruments of Mind
An Affective Module for an Intelligent Tutoring System
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Toward computers that recognize and respond to user emotion
IBM Systems Journal
Affective Learning — A Manifesto
BT Technology Journal
Comparing two approaches to context: realism and constructivism
Proceedings of the 4th decennial conference on Critical computing: between sense and sensibility
Real-time estimation of emotional experiences from facial expressions
Interacting with Computers
Empathic agents to reduce user frustration: The effects of varying agent characteristics
Interacting with Computers
Modeling User Affect from Causes and Effects
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Towards mood based mobile services and applications
EuroSSC'07 Proceedings of the 2nd European conference on Smart sensing and context
Social navigation with the collective mobile mood monitoring system
Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments
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Expression Glasses provide a wearable "appliance-based" alternative to general-purpose machine vision face recognition systems. The glasses sense facial muscle movements, and use pattern recognition to identify meaningful expressions such as confusion or interest. A prototype of the glasses has been built and evaluated. The prototype uses piezoelectric sensors hidden in a visor extension to a pair of glasses, providing for compactness, user control, and anonymity. On users who received no training or feedback, the glasses initially performed at 94% accuracy in detecting an expression, and at 74% accuracy in recognizing whether the expression was confusion or interest. Significant improvement beyond these numbers appears to be possible with extended use, and with-a small amount of feedback (letting the user see the output of the system).