Open AccessHand Gesture Recognition-Based Control of Motorized Wheelchair using Electromyography Sensors and Recurrent Neural Network
Author(s) -
Oyeronke ADEBAYO,
Emmanuel Adetiba,
Oluwaseun T. Ajayi
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1107/1/012063
Subject(s) - wheelchair , gesture , computer science , gesture recognition , recurrent neural network , software deployment , control (management) , physical medicine and rehabilitation , population , artificial neural network , artificial intelligence , medicine , environmental health , world wide web , operating system
Mobility has been identified to be a major characteristic of living things. Humans who are deprived of efficient mobility either by natural or man-made factors loose significant relationship with their environment. The growing demand to produce effective rehabilitation devices for the aged population and disabled individuals, have spurred us to develop a reliable and easy to use biosignal based auto control wheelchair. This is to ensure independent mobility of persons with disabilities and the aged. In this paper, a Recurrent Neural Network (RNN) architecture called Long Short Term Memory (LSTM) is engaged for the classification EMG signals to the corresponding hand-gesture category. The LSTM model in this study yielded a validation accuracy that provides a basis for an improved solution towards real-time deployment.