Automated Posture Analysis for detecting Learner’s Interest Level

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 feedforward 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. 1