Sign language trainer using leap motion

Christian Feicán; Julio Cabrera; John Arévalo; Edy Ayala; Fernando Guerrero; Eduardo Pinos

doi:10.1007/978-3-319-60018-5_25

Sign language trainer using leap motion

Christian Feicán, Julio Cabrera, John Arévalo, Edy Ayala, Fernando Guerrero, Eduardo Pinos

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Learning a sign language can be a long process, but necessary to improve the communication of people, nowadays you need face-to-face courses. To facilitate the learning of Ecuadorian Sign Language and make it more interactive, a software trainer is proposed using the Leap Motion® device. The system is based on an SVM classifier, the parameters were calculated to obtain greater efficiency in the classifier. It is able to predict signs from A to Z including mobile signs, the Ecuadorian typographic alphabet has 30 letters and 5 require movement. The system was trained with a database of 40 people resulting in 1200 signs.

Original language	English (US)
Title of host publication	Sign language trainer using leap motion
Editors	Terence Andre
Pages	253-264
Number of pages	12
ISBN (Electronic)	9783319600178
DOIs	https://doi.org/10.1007/978-3-319-60018-5_25
State	Published - 1 Jan 2018
Event	Advances in Intelligent Systems and Computing - , Germany Duration: 1 Jan 2015 → …

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	596
ISSN (Print)	2194-5357

Conference

Conference	Advances in Intelligent Systems and Computing
Country/Territory	Germany
Period	1/01/15 → …

Keywords

Ecuadorian Sign Language
Human factors in education
Human factors in training
Leap motion
Support vector machine

Access to Document

10.1007/978-3-319-60018-5_25

Cite this

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title = "Sign language trainer using leap motion",

abstract = "Learning a sign language can be a long process, but necessary to improve the communication of people, nowadays you need face-to-face courses. To facilitate the learning of Ecuadorian Sign Language and make it more interactive, a software trainer is proposed using the Leap Motion{\textregistered} device. The system is based on an SVM classifier, the parameters were calculated to obtain greater efficiency in the classifier. It is able to predict signs from A to Z including mobile signs, the Ecuadorian typographic alphabet has 30 letters and 5 require movement. The system was trained with a database of 40 people resulting in 1200 signs.",

keywords = "Ecuadorian Sign Language, Human factors in education, Human factors in training, Leap motion, Support vector machine",

author = "Christian Feic{\'a}n and Julio Cabrera and John Ar{\'e}valo and Edy Ayala and Fernando Guerrero and Eduardo Pinos",

year = "2018",

month = jan,

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doi = "10.1007/978-3-319-60018-5_25",

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isbn = "9783319600178",

series = "Advances in Intelligent Systems and Computing",

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booktitle = "Sign language trainer using leap motion",

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T1 - Sign language trainer using leap motion

AU - Feicán, Christian

AU - Cabrera, Julio

AU - Arévalo, John

AU - Ayala, Edy

AU - Guerrero, Fernando

AU - Pinos, Eduardo

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Learning a sign language can be a long process, but necessary to improve the communication of people, nowadays you need face-to-face courses. To facilitate the learning of Ecuadorian Sign Language and make it more interactive, a software trainer is proposed using the Leap Motion® device. The system is based on an SVM classifier, the parameters were calculated to obtain greater efficiency in the classifier. It is able to predict signs from A to Z including mobile signs, the Ecuadorian typographic alphabet has 30 letters and 5 require movement. The system was trained with a database of 40 people resulting in 1200 signs.

AB - Learning a sign language can be a long process, but necessary to improve the communication of people, nowadays you need face-to-face courses. To facilitate the learning of Ecuadorian Sign Language and make it more interactive, a software trainer is proposed using the Leap Motion® device. The system is based on an SVM classifier, the parameters were calculated to obtain greater efficiency in the classifier. It is able to predict signs from A to Z including mobile signs, the Ecuadorian typographic alphabet has 30 letters and 5 require movement. The system was trained with a database of 40 people resulting in 1200 signs.

KW - Ecuadorian Sign Language

KW - Human factors in education

KW - Human factors in training

KW - Leap motion

KW - Support vector machine

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DO - 10.1007/978-3-319-60018-5_25

M3 - Conference contribution

SN - 9783319600178

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ER -

Sign language trainer using leap motion

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