Fuzzy ARTMAP based feature classification for danger and safety zone prediction for toddlers using wearable electrodes

The desired performance of every childcare and monitoring system is to clearly read the user activity into a relevant category of the solution domain. This categorization highly depends on error free processing methods and systematic regression or classification. The wearable interface acquires multiple signals of the user activity that serves as the input to the monitoring system. The pattern of the signal array after necessary consolidation and feature processing, determines its candidature into defined classes. Hence it is crucial to deploy a strong classifier which can characterize the activity of the user into normal zone activities or dangerous. In this paper, we used the robust and adroitness classification model Fuzzy ARTMAP to classify signals from wearable interface for augmenting the accuracy of the child monitoring system. The Fuzzy ARTMAP is an ART network for the association of analogy pattern in supervised mode and is capable of overcoming the stability-Plasticity dilemma. In our experiments, the arrays of sensor signals extracted from the wearable interface during monitoring process from toddlers are classified using the feature signal pattern. The high accuracy obtained as classification percentages validates the suitability of our proposed Fuzzy ARTMAP classification for such critical real time system.