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Design of electrical wheelchair navigation for disabled patient using convolutional neural networks on Raspberry Pi 3 by Sutikno, ;Anam, Khairul ;Sujanarko, Bambang is a Physics and Astronomy article available to read on EtoBox.

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Patients with physical disabilities such as losing an arm, hand, or paralysis will have difficulty moving from one place to another. They need someone or a device that can help their mobility. One of the tools that are often used by physically disabled patients to help their activities is an electric wheelchair. The main purpose of this article is to design a wheelchair for physically disabled patients that can be controlled by voice commands using the convolutional neural network method (CNN). CNN is employed to identify voice commands embedded on raspberry Pi 3. The recorded sound data is converted to spectrogram images before being fed to CNN. This method has been proven well in voice commands recognition with an accuracy of more than 90%. There are five different voice commands for wheelchair navigation, which are forward, backward, left, right and stop. Preliminary experimental results indicate that electrically designed wheelchairs can move using speech command.

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Author

Sutikno, ;Anam, Khairul ;Sujanarko, Bambang

Publisher

AIP Publishing

Published

2020

Language

EN

Field

Physics and Astronomy (Physical Sciences)