Offline study for implementing human computer interface for elderly paralysed patients using electrooculography and neural networks
by S. Ramkumar; K. Sathesh Kumar; K. Maheswari; P. Packia Amutha Priya; G. Emayavaramban; J. Macklin Abraham Navamani
International Journal of Intelligent Enterprise (IJIE), Vol. 7, No. 1/2/3, 2020

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 lefthander performance was marginally appreciated compared to right hander performance in terms of classification accuracy with an average accuracy of 93.38% for all left hand subjects and 91.38% for all the right subjects using probabilistic neural network (PNN) and also we analysed that during the training left handers were interestingly participated and also they can able to perform the following eleven tasks easily compared with right handers.

Online publication date: Mon, 27-Jan-2020

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