Automated Posture Analysis for Detecting Learner's Interest Level

• Published in: 2003 Conference on Computer Vision and Pattern Recognition Workshop Vol. 5; p. 49
• Main Authors: Mota, Selene, Picard, Rosalind W.
• Format: Conference Proceeding
• Language: English; Japanese
• Published: IEEE 01.06.2003
• Subjects: Artificial neural networks; Computational modeling; Hidden Markov models; Mathematical model; Pediatrics; Sensors; Training
• ISBN: 0769519008; 9780769519005
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPRW.2003.10047

This paper presents a system for recognizing naturally occurring postures and associated affective states related to a child's interest level while performing a learning task on a computer. Postures are gathered using two matrices of pressure sensors mounted on the seat and back of a chair. Subsequently, posture features are extracted using a mixture of four gaussians, and input to a 3-layer feed-forward neural network. The neural network classifies nine postures in real time and achieves an overall accuracy of 87.6% when tested with postures coming from new subjects. A set of independent Hidden Markov Models (HMMs) is used to analyze temporal patterns among these posture sequences in order to determine three categories related to a child's level of interest, as rated by human observers. The system reaches an overall performance of 82.3% with posture sequences coming from known subjects and 76.5% with unknown subjects.