Stable and crit gesticulation recognition in children and pregnant women by Naïve Bayes classification

Abstract

The remote healthcare monitoring on a care taking base involves many implicit observations between the subjects and the care takers. Any ignorance and negligence leads to unpleasant situations thereafter. A wearable attire system can precisely interpret the implicit communication of the state of the subject and pass it to the care takers or to an automated aid device. Casual and conventional movements of subjects during play and living condition can be used for the above purpose. The proposed system suggests a novel way of identifying safe and unsafe conditions of playing for the children where a rapid warning assistance is required. The Same in the case of the normal and contraction time identification of pregnant women. Naive Bayes classifier was applied on five different sets of combinations of features constructed by Fractal Dimension, Fast Fourier Transformation, Singular Value Decomposition and combinations of the three. The result shows the combinational features of FFT and SVD are more supportive in all three sets of experiments and better classified by Navie Bayes classifier than the other combination and individual features. The experimental results show a well-distinguished realization of different body movement activities using a wearable attire array medium and the interpretation results always show significant and identifiable thresholds.