  In the context of education technology, empathic interaction with the user
and feedback by the learning system using multiple inputs such as video, voice
and text inputs is an important area of research. In this paper, a
nonintrusive, standalone model for intelligent assessment of alertness and
emotional state as well as generation of appropriate feedback has been
proposed. Using the non-intrusive visual cues, the system classifies emotion
and alertness state of the user, and provides appropriate feedback according to
the detected cognitive state using facial expressions, ocular parameters,
postures, and gestures. Assessment of alertness level using ocular parameters
such as PERCLOS and saccadic parameters, emotional state from facial expression
analysis, and detection of both relevant cognitive and emotional states from
upper body gestures and postures has been proposed. Integration of such a
system in e-learning environment is expected to enhance students performance
through interaction, feedback, and positive mood induction.
