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In this work, a methodology for the classification of breathing patterns in order to prevent sudden infant death (SID) incidents is presented. The basic idea is to classify breathing patterns which might lead to SID prior to an incident. A thorax sensor is proposed, which is able to simulate breathing patterns given by certain parameters. A sensor combination of conductive strain fabric and an inertial measurement unit is used for data acquisition. The data is then classified using a neural network.

Simulation of Breathing Patterns and Classification of Sensor Data for the early detection of impending Sudden Infant Death