Title
Probabilistic Prediction of Student Affect from Hand Gestures
Abstract
Affective information is vital for effective human-to- human communication. Likewise, human-to-computer communi- cation 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 include cases where the student reported no affective state. The experiment demonstrates that there is a strong relationship between human affect and visually observable gestures. Future work will explore the applicability of these results in practical applications. Index Terms— Behavior recognition, Intelligent tutoring sys- tems, Human-computer interaction, Probabilistic affect predic- tion, Unintentional hand gestures.
Year
Venue
Keywords
2008
ARCS
machine vision,indexing terms,human computer interaction,cross validation,bayesian network
Field
DocType
Citations 
Computer science,Gesture,Real-time computing,Human–computer interaction,Artificial intelligence,Probabilistic logic,Classifier (linguistics),Machine vision system,Bayesian network,Maximum a posteriori estimation,Affect (psychology),Cross-validation,Machine learning
Conference
0
PageRank 
References 
Authors
0.34
10
4
Name
Order
Citations
PageRank
Abdul Rehman Abbasi1463.33
Matthew N. Dailey233126.44
Nitin V. Afzulpurkar3505.44
Takeaki Uno41319107.99