Wheelchair Movement Based on Convolution Neural Network | ||
| Engineering and Technology Journal | ||
| Article 14, Volume 39, Issue 6, June 2021, Pages 1019-1030 PDF (1.24 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.30684/etj.v39i6.1615 | ||
| Authors | ||
| Maisaa Kodher1; Jammila H. Saud2; Hani S. Hassan* 1 | ||
| 1Computer science Department, University of Technology - Iraq | ||
| 2Computer science Department, Mustansiriyah University - Iraq | ||
| Abstract | ||
| This paper intends to develop a methodology for helping amputees and crippled people old, by ongoing voice direction and association between patient and personal computer (PC) where these blends offer a promising response for helping the debilitated people. The major objective of this work is accurately detected audio orders via a microphone of an English language (go, stop, right and left) in a noisy environment by the proposed system. Thus, a patient that utilizes the proposed system can be controlling a wheelchair movement. The venture depends on preparing an off-line dataset of audio files are included 10000 orders and background noise. The proposed system has two important steps of preprocessing to get accurate of specific audio orders, accordingly, the accurate direction of wheelchair movement. Firstly, a dataset was preprocessed to reduce ambient noise by using Butterworth (cutoff 500-5000 Hz) and Wiener filter. Secondly, in the input (a microphone) of the proposed discriminative model put a procedure of infinite impulse response filter (Butterworth), passband filter for cutoff input microphone from 150-7000 Hz for back-off the loud and environment noise and local polynomial approximation (Savitzky-Golay) smoothing filter that plays out a polynomial regression on the signal values. Thus, a better for filtering from ambient noise and keeping on a waveform from distortion that makes the discriminative model accurate when voice orders were recognized. The proposed system can work with various situations and speeds for steering; forward, stop, left and right. All datasets are trained by using deep learning with specific parameters of a convolutional neural network (CNN). These capacities are dependent on code written in MATLAB. The prototype uses Arduino UNO and a microphone (MIC) | ||
| Keywords | ||
| Wheelchair; Deep learning; amputee; disable people; Arduino Uno | ||
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