The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Automated Posture Analysis for Detecting Learner's Interest Level
Abstract
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.
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Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
Observing Interaction: An Introduction to Sequential Analysis
Bakeman, R., & Gottman, J. M. (1997) · doi:10.1017/cbo9780511527685 · Cited by 1,439
Cited by
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
Calvo, R. A., & D'Mello, S. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.1 · Cited by 1,893
Social signal processing: Survey of an emerging domain
Vinciarelli, A., Pantic, M., & Bourlard, H. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.11.007 · Cited by 842
Fundamentals of physiological computing
Fairclough, S. H. (2009) · Interacting with Computers · doi:10.1016/j.intcom.2008.10.011 · Cited by 608
Automatic prediction of frustration
Kapoor, A., Burleson, W., & Picard, R. W. (2007) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2007.02.003 · Cited by 592
Self-citationToward an Affect-Sensitive AutoTutor
D'Mello, S., Picard, R. W., & Graesser, A. (2007) · IEEE Intelligent Systems · doi:10.1109/mis.2007.79 · Cited by 386
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Metadata
- Authors
- Mota, S., & Picard, R. W.
- Venue
- 2003 Conference on Computer Vision and Pattern Recognition Workshop
- Publisher
- IEEE
- Published
- 2003-06
- Pages
- 49-49
- OpenAlex
- W2136091964
- Cited by
- 314
- References
- 3
- FWCI
- 7.16
- Type
- conference paper
- Language
- en
- Open access
- closed
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