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Cited by 314

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

Mota, Selene, Picard, Rosalind W.·2003·2003 Conference on Computer Vision and Pattern Recognition Workshop

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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Crossref · 10.1109/cvprw.2003.10047

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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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CoChat. (2026). Citation check: Automated Posture Analysis for Detecting Learner's Interest Level. https://app.cochat.ai/papers/10.1109/cvprw.2003.10047
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Mota, S., & Picard, R. W. (2003). Automated Posture Analysis for Detecting Learner's Interest Level. 2003 Conference on Computer Vision and Pattern Recognition Workshop. https://doi.org/10.1109/cvprw.2003.10047

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