Gesture-Based Affect Modeling for Intelligent Tutoring Systems

Bustos, Dana May; Chua, Geoffrey Loren; Cruz, Richard Thomas; Santos, José Miguel Rodríguez; Suarez, Merlin Teodosia

doi:10.1007/978-3-642-21869-9_58

Cited by 3 publications

(2 citation statements)

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“…The importance of hand gestures and posture for modeling the affective state of a student in the context of an ITS is presented in [48]. The spontaneous gestures during the performance of academic tasks are studied and connected to 4 different emotions: boredom, flow, confusion, frustration.…”

Section: Gesture Recognitionmentioning

confidence: 99%

Researching affective computing techniques for intelligent tutoring systems

Ivanova

2013

2013 International Conference on Interactive Collaborative Learning (ICL)

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Intelligent Tutoring is applied as a supportive tool in decision making, problem solving, knowledge receiving. Adding computer vision for affect recognition leads to the adaptation of tutor behavior not only to the cognitive level of a student but also to his emotional state that could improve quality of learning. In this paper the current research in the area of facial expression and gesture recognition in the context of intelligent tutoring is examined with the aim to facilitate educational society in building of affective intelligent tutoring systems.

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Section: Gesture Recognitionmentioning

confidence: 99%

Researching affective computing techniques for intelligent tutoring systems

Ivanova

2013

2013 International Conference on Interactive Collaborative Learning (ICL)

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“…As an accessible means of computer input, gesture recognition has been studied intensively [1] and gesture interfaces are actively being developed. Additional studies on the role of gestures in affective communication and their associations with emotion in interaction with information systems have also been conducted [2]. They are also used to make humanoid robots and software agents appear more realistic or natural to human [3].…”

Section: Introductionmentioning

confidence: 99%

Speed dependencies of human gesture recognition

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Inoue

2013

2013 International Joint Conference on Awareness Science and Technology &Amp; Ubi-Media Computing (iCAST 2013 &Amp; UMEDIA 2013

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Gestures are important communication medium. However, their semantic ambiguities make it difficult for computer systems to accurately recognize and deliver them. The interpretation of gestural motions is not even consistent in human-to-human communications. In this paper, we compare gestures in order to establish a method to reveal the influence of gesture speed on semantic interpretation. We captured whole-body motions as movie files with a depth camera and converted them into biological motion movie files. The speed of these motion images was then systematically altered, and a perceptual experiment was conducted on 11 participants. Using the results from the experiment, we identified speed dependencies of human gesture recognition.

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