Baby-Posture Classification from Pressure-Sensor Data

The activity of babies and more specifically the posture of babies is an important aspect in their safety and development. In this paper, we studied the automatic classification of baby posture using a pressure-sensitive mat. The posture classification problem is formulated as the design of features that describe the pressure patterns induced by the child in combination with generic classifiers. Novel rotation invariant features constructed from high order statistics obtained from the concentric rings around the center of gravity. Non-constant ring radii are used in order to ensure uniform cell areas and therefore equal importance of features. A vote fusion of various generic classifiers is used for classification. Temporal information was shown to improve the classification performance. The obtained results are promising and open new opportunities for applications and further research in the area of baby safety and development.