Stable and Critical Gesture Recognition in Children and Pregnant Women by SVM Classification with FFT Features of Signals from Wearable Attires

Abstract

The aim is to identify stable and critical recognition in children and pregnant women by wearable interface based monitoring system. The objective of this research proposal is to identify safe and danger zone prediction in toddler and normal and contra pain in pregnant women. The technology supporting this analysis of gesture has advanced dramatically. Past decades of remote health care monitoring have provided us with significant knowledge about the accuracy of tests performed. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature bio sensing devices, smart textiles, microelectronics and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a toddler and pregnant women health condition.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 simple 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 methodology suggests a novel way of identifying safe and unsafe conditions of playing for the children as well as normal and critical situations of pregnant women where a medical assistance is desperately required. 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.