Probabilistic Prediction of Student Affect from Hand Gestures

Abstract-- Affective information is vital for effective human-tohuman communication. Likewise, human-to-computer communication could be potentiated by an "affective barometer" able to infer human affect using a machine vision system. For instance, during a classroom lecture, an affective barometer might provide useful feedback that a real or virtual instructor could use to improve pedagogical strategies. In this paper, we explore the feasibility of using students' unintentional hand gestures during a classroom lecture to predict their affective state. We propose a maximum a posteriori classifier based on a simple Bayesian network model. We then evaluate the classifier's ability to predict one of four affective states from five hand gestures observed in video recordings of a classroom lecture. Using four-fold cross validation, we find that the model's generalization accuracy is 100% over cases where the student reported an affective state, and 79.4% when we incl...

• Type: Conference
• Year: 2008
• Where: ARCSF
• Authors: Abdul Rehman Abbasi, Matthew N. Dailey, Nitin V. Afzulpurkar, Takeaki Uno
• Keywords: Affective, Affective Barometer, Affective State, ARCSF 2008, Control Systems