Embedded system on high performance data for wearable augmented reality of eye blinks, muscle stress detection movement and observation

Abstract

Eyes blinking and its movement can portray many reasons of the body and health state. Eyes can blink intentionally and sometimes randomly even in sleeping mode. Thus, the aim of this paper is to discover and observe the relationship between the frequency of eye blink and the level of eye muscle stress. The eye track data is fed directly into the electroencephalogram (EEG) record for parameter classification and identification. The EEG signal might have an artifact that has been analyzed and converted the observation into the mathematical library and repository software (HPC). The artificial neural network (ANN) is integrated with EEG digital data by the derivation of the mathematical modelling. The function of ANN is to train a large sparse digital data for future prediction of eye condition associated with the stress level. In order to validate the model and simulation, the numerical analysis and performance evaluation are compared to the real data set of eye therapy industry, IC Herbz Sdn Bhd. A library and repository software of mathematical model using EEG record data is developed to integrate with wearable augmented reality (WAR) based on EEG sensor device for predicting and monitoring the real time eye blinks, movement and muscle stress.