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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    Title :

    Automated Posture Analysis for Detecting Learner's Interest Level


    Contributors:


    Publication date :

    2003-06-01


    Size :

    284124 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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