Real time academic emotion recognition using body gestures
Date of Publication
2012
Document Type
Master's Thesis
Degree Name
Master of Science in Computer Science
College
College of Computer Studies
Department/Unit
Computer Science
Thesis Adviser
Merlin Suarez
Abstract/Summary
Emotion plays a powerful role in students learning process, however, many intelligent tutoring systems (ITS) only consider the knowledge models of the students. Hence, building affect models for ITSs have been an emerging research researchers had proposed several approaches on affect recognition based on the students facial expressions, speech and body gestures. However, most of these works focused on gathering emotional information from facial expressions and speech of the students. Acted emotion databases and expensive hardware are also used to predict student emotions. In this paper, a markerless approach is proposed to detect four basic academic emotions (boredom, flow, confusion, frustration) from the student based on their body gestures. The data for this study were gathered from five students using Microsoft Kinect camera. Features extracted were hand position and duration, arm position, speed, head tilt, leaning and shifting. Given the features extracted, the gestures detected by the system include hands up/hands near face/hands down, arms up/arms down, scratch/steady, fast/slow, lean forward/lean backward, lean right/lean left, shifting/not shifting. To classify the emotion of the student, rules were generated based on the students gestures. The system generated five rules for each students, the system achieved an accuracy of 72.57%, 91.06% , 76.42% , 77.91% and 81.96% for each student respectively.
Abstract Format
html
Language
English
Format
Accession Number
TG05246; CDTG005246
Shelf Location
Archives, The Learning Commons, 12F Henry Sy Sr. Hall
Physical Description
1 v. (various foliations) ; 28 cm. + 1 computer optical disc.
Keywords
Emotion recognition
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Recommended Citation
Cheung, O. (2012). Real time academic emotion recognition using body gestures. Retrieved from https://animorepository.dlsu.edu.ph/etd_masteral/4288