Ilham A.E. Zaeni, Arizal Ismoyo Wijanarko, Ahsan Walad, An Qi-Sheng, Anik N. Handayani
Some people with quadriplegia which cannot move their part of the body from hand to feet need an assistive device to support their mobility. An assistive device in form of an electric wheelchair can be developed using eye activity signals. The Artificial Neural Network (ANN) is proposed to be implemented on wheelchair control system based the user eye movement command. The system consistsof the eye movement electrode, data collection, signal filtering and pre-processing, and the decision model. There are four commands that is involved on this study. The commands are glances left, glances right, blink, and double blink for command turn left, turn right, stop, and going forward, respectively. The hold out used for validation model by splitting data into 80% training set and 20% testing set. The test result shows that the Mean Absolute Percentage Error (MAPE) of the decision model is 1.55%. This result is a good result and the model can be implemented on the system. © 2020 IEEE.
Universitas Negeri Malang, Dept. of Electrical Engineering, Malang, Indonesia; Southern Taiwan University of Science and Technology, Dept. of Electrical Engineering, Tainan, Taiwan
