Gesture-based Affect Modeling for Intelligent Tutoring Systems

Authors: 

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

Paper type: 
Poster

Abstract: 

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.

Keywords: 

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