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.
Cite
Text
Mota and Picard. "Automated Posture Analysis for Detecting Learner's Interest Level." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2003. doi:10.1109/CVPRW.2003.10047Markdown
[Mota and Picard. "Automated Posture Analysis for Detecting Learner's Interest Level." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2003.](https://mlanthology.org/cvprw/2003/mota2003cvprw-automated/) doi:10.1109/CVPRW.2003.10047BibTeX
@inproceedings{mota2003cvprw-automated,
title = {{Automated Posture Analysis for Detecting Learner's Interest Level}},
author = {Mota, Selene and Picard, Rosalind W.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2003},
pages = {49},
doi = {10.1109/CVPRW.2003.10047},
url = {https://mlanthology.org/cvprw/2003/mota2003cvprw-automated/}
}