Devices Analysis And Artificial Neural Network Parameters for Sign Language Recognition

被引：0

作者：

Silva, Brunna ^{[1
]}

Calixto, Wesley ^{[2
]}

Furriel, Geovanne ^{[3
]}

机构：

[1] Fed Inst Goias, Acad Dept, Senador Canedo, Brazil

[2] Fed Inst Goias, Acad Dept, Goiania, Go, Brazil

[3] Goiano Fed Inst, Acad Dept, Trindade, Brazil

来源：

2017 CHILEAN CONFERENCE ON ELECTRICAL, ELECTRONICS ENGINEERING, INFORMATION AND COMMUNICATION TECHNOLOGIES (CHILECON) | 2017年

关键词：

sign language; artificial neural network; learning rate; multilayer perceptron; deaf person; micromechanical devices; flex sensor; accelerometer; gyroscope;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The purpose of this paper is to develop and analyses device capable of identifying sign language. The recognition is performed using Multilayer Perceptron and all the input data are signals from flex sensors, accelerometers and gyroscopes. Artificial Neural Network is tested modifying parameters as: a) number of neurons in only middle layer, b) learning rate between input and middle layers and c) learning rate between middle and output layers. After being trained, validated and tested, the network reachs hit rate about 96.1%. It is proposed as alternative to deaf people's accessibility and solution with good accuracy and low financial cost compared to those devices already on the market.

引用

页数：5

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