Stable and Critical Gesticulation Recognition in Children and Pregnant Women by Weighted Naive Bayes Classification

The healthcare monitoring on a remote care taking base involves many implicit observations between the subjects and the care takers. Any deficit in domain knowledge and carelessness 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 system is used in the case of the normal and contraction time identification of pregnant women. Naive Bayes classifier was applied on features created by different algorithms and on the combinations of features constructed by algorithms like Fractal Dimension, Fast Fourier Transformation, Singular Value Decomposition. The result shows in general that the combinational features with point system results in better classification. Especially the FFT and SVD were more supportive in all three sets of experiments and better classified by Navie Bayes classifier than the other combinations and individual features. But the complexity is high when going through the point system. When a priori based point system is introduced with a reduced complexity to replace the conventional point system, the enhanced results show a well-distinguished realization of different body movement activities using a wearable attire array and the interpretation consistently results in significant and identifiable thresholds.