Task Identification System for Elderly Paralyzed Patients Using Electrooculography and Neural Networks

Abstract

Earlier day’s people with disability face lot of difficulty in communication due to neuromuscular attack. They are unable to share ideas and thoughts with others, so they need some assist to overcome this condition. To overcome the condition in this paper we discussed the capabilities of designing electrooculogram (EOG)-based human computer interface (HCI) by ten subjects using power spectral density techniques and Neural Network. In this study we compare the right-hander performance with left-hander performance. Outcomes of the study concluded that left-hander performance was marginally appreciated compared to right-hander performance in terms of classification accuracy with an average accuracy of 93.38% for all left-hander subjects and 91.38% for all the right subjects using probabilistic neural network (PNN) and also we analyzed that during the training left-handers were interestingly participated and also they can able to perform the following 11 tasks easily compared with right-handers. From this study we concluded that potentiality of creating HCI was possible by means of left-handers and also study proves that right-hander need some more training to achieve this. Finally the experiment outperforms our previous study in terms of performance by changing the subjects from right-hander to left-handers.