Gesture-based Affect Modeling for Intelligent Tutoring Systems


Dana May Bustos, Geoffrey Loren Chua, Richard Thomas Cruz, Jose Miguel Santos and Merlin Suarez

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This paper investigates the feasibility of using gestures and posture for building affect models for an ITS. Recordings of students studying with a computer were taken and an HMM was built to recognize gestures and posture. Results indicate distinctions can be achieved with an accuracy of 43.10% using leave-one out cross validation. Results further indicate the relevance of hand location, movement and speed of movement as features for affect modeling using gestures and posture.


Gesture recognition, affect modeling, intelligent tutoring systems, emotions in gestures