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ARCSF
2008

Probabilistic Prediction of Student Affect from Hand Gestures

9 years 8 months ago
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...
Abdul Rehman Abbasi, Matthew N. Dailey, Nitin V. A
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where ARCSF
Authors Abdul Rehman Abbasi, Matthew N. Dailey, Nitin V. Afzulpurkar, Takeaki Uno
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