Colombian Sign Language Interpretation Model using Artificial Intelligence
In this work, two interpretation models of Colombian Sign Language (CSL) are presented, using static and dynamic methods that employ artificial intelligence. The CRISP-DM methodology was used as a reference, creating a database with videos from seventy non-expert participants, being preprocessed and...
- Autores:
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_6739
- Fecha de publicación:
- 2023
- Institución:
- Universidad Pedagógica y Tecnológica de Colombia
- Repositorio:
- RiUPTC: Repositorio Institucional UPTC
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uptc.edu.co:001/10432
- Acceso en línea:
- https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16840
https://repositorio.uptc.edu.co/handle/001/10432
- Palabra clave:
- colombian sign language;
CNN;
LSTM;
CRISP-DM
lengua de señas colombiano;
CNN;
LSTM;
CRISP-DM
- Rights
- License
- Derechos de autor 2023 Revista de Investigación, Desarrollo e Innovación
id |
REPOUPTC2_caaac2383614c07518749d40a4eb3233 |
---|---|
oai_identifier_str |
oai:repositorio.uptc.edu.co:001/10432 |
network_acronym_str |
REPOUPTC2 |
network_name_str |
RiUPTC: Repositorio Institucional UPTC |
repository_id_str |
|
spelling |
2023-08-152024-07-05T18:04:17Z2024-07-05T18:04:17Zhttps://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/1684010.19053/20278306.v13.n2.2023.16840https://repositorio.uptc.edu.co/handle/001/10432In this work, two interpretation models of Colombian Sign Language (CSL) are presented, using static and dynamic methods that employ artificial intelligence. The CRISP-DM methodology was used as a reference, creating a database with videos from seventy non-expert participants, being preprocessed and subsequently divided into proportions of 70% - 30% for training and testing, respectively. The repository was named LSC-W70 and was used on a pre-trained model of convolutional neural networks and another in combination with LSTM networks. The results reached 67% and 76% accuracy for the static and dynamic models, respectively, where the dynamic model presents improvements in similar signs by identifying the direction of movement to define the type of sign. In this sense, a dynamic Colombian sign language interpretation tool was developed that helps close communication gaps, generating equality between people.En este trabajo se presentan dos modelos de interpretación de Lengua de Señas Colombiana (LSC), usando métodos estáticos y dinámicos que emplean inteligencia artificial. Se utilizó como referente la metodología CRISP-DM, creando una base de datos con videos de setenta participantes no expertos, siendo preprocesados y posteriormente divididos en proporciones de 70% - 30% para entrenamiento y prueba, respectivamente. El repositorio se nombró como LSC-W70 y se empleó sobre un modelo preentrenado de redes neuronales convolucionales y otro en combinación con redes LSTM. Los resultados alcanzaron un 67% y 76% accuracy para los modelos estático y dinámico, respectivamente, donde el modelo dinámico presenta mejoras en señas similares identificando la dirección del movimiento para definir el tipo de seña. En este sentido, se desarrolló una herramienta de interpretación dinámica de lengua de señas colombiano que ayuda a cerrar brechas de comunicación generando igualdad entre las personas.application/pdftext/xmlspaspaUniversidad Pedagógica y Tecnológica de Colombiahttps://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16840/13652https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16840/13935Derechos de autor 2023 Revista de Investigación, Desarrollo e Innovaciónhttp://purl.org/coar/access_right/c_abf240http://purl.org/coar/access_right/c_abf2Revista de Investigación, Desarrollo e Innovación; Vol. 13 No. 2 (2023): Julio-Diciembre; 357-366Revista de Investigación, Desarrollo e Innovación; Vol. 13 Núm. 2 (2023): Julio-Diciembre; 357-3662389-94172027-8306colombian sign language;CNN;LSTM;CRISP-DMlengua de señas colombiano;CNN;LSTM;CRISP-DMColombian Sign Language Interpretation Model using Artificial IntelligenceModelo de interpretación de lengua de señas colombiano usando inteligencia artificialinfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6739http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a323http://purl.org/coar/version/c_970fb48d4fbd8a85Muñoz-Galindez, Jader AlejandroVargas-Cañas, Rubiel001/10432oai:repositorio.uptc.edu.co:001/104322025-07-18 11:51:36.615metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCrepositorio.uptc@uptc.edu.co |
dc.title.en-US.fl_str_mv |
Colombian Sign Language Interpretation Model using Artificial Intelligence |
dc.title.es-ES.fl_str_mv |
Modelo de interpretación de lengua de señas colombiano usando inteligencia artificial |
title |
Colombian Sign Language Interpretation Model using Artificial Intelligence |
spellingShingle |
Colombian Sign Language Interpretation Model using Artificial Intelligence colombian sign language; CNN; LSTM; CRISP-DM lengua de señas colombiano; CNN; LSTM; CRISP-DM |
title_short |
Colombian Sign Language Interpretation Model using Artificial Intelligence |
title_full |
Colombian Sign Language Interpretation Model using Artificial Intelligence |
title_fullStr |
Colombian Sign Language Interpretation Model using Artificial Intelligence |
title_full_unstemmed |
Colombian Sign Language Interpretation Model using Artificial Intelligence |
title_sort |
Colombian Sign Language Interpretation Model using Artificial Intelligence |
dc.subject.en-US.fl_str_mv |
colombian sign language; CNN; LSTM; CRISP-DM |
topic |
colombian sign language; CNN; LSTM; CRISP-DM lengua de señas colombiano; CNN; LSTM; CRISP-DM |
dc.subject.es-ES.fl_str_mv |
lengua de señas colombiano; CNN; LSTM; CRISP-DM |
description |
In this work, two interpretation models of Colombian Sign Language (CSL) are presented, using static and dynamic methods that employ artificial intelligence. The CRISP-DM methodology was used as a reference, creating a database with videos from seventy non-expert participants, being preprocessed and subsequently divided into proportions of 70% - 30% for training and testing, respectively. The repository was named LSC-W70 and was used on a pre-trained model of convolutional neural networks and another in combination with LSTM networks. The results reached 67% and 76% accuracy for the static and dynamic models, respectively, where the dynamic model presents improvements in similar signs by identifying the direction of movement to define the type of sign. In this sense, a dynamic Colombian sign language interpretation tool was developed that helps close communication gaps, generating equality between people. |
publishDate |
2023 |
dc.date.accessioned.none.fl_str_mv |
2024-07-05T18:04:17Z |
dc.date.available.none.fl_str_mv |
2024-07-05T18:04:17Z |
dc.date.none.fl_str_mv |
2023-08-15 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6739 |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.coarversion.spa.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a323 |
format |
http://purl.org/coar/resource_type/c_6739 |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16840 10.19053/20278306.v13.n2.2023.16840 |
dc.identifier.uri.none.fl_str_mv |
https://repositorio.uptc.edu.co/handle/001/10432 |
url |
https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16840 https://repositorio.uptc.edu.co/handle/001/10432 |
identifier_str_mv |
10.19053/20278306.v13.n2.2023.16840 |
dc.language.none.fl_str_mv |
spa |
dc.language.iso.spa.fl_str_mv |
spa |
language |
spa |
dc.relation.none.fl_str_mv |
https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16840/13652 https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16840/13935 |
dc.rights.es-ES.fl_str_mv |
Derechos de autor 2023 Revista de Investigación, Desarrollo e Innovación |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf240 |
rights_invalid_str_mv |
Derechos de autor 2023 Revista de Investigación, Desarrollo e Innovación http://purl.org/coar/access_right/c_abf240 http://purl.org/coar/access_right/c_abf2 |
dc.format.none.fl_str_mv |
application/pdf text/xml |
dc.publisher.es-ES.fl_str_mv |
Universidad Pedagógica y Tecnológica de Colombia |
dc.source.en-US.fl_str_mv |
Revista de Investigación, Desarrollo e Innovación; Vol. 13 No. 2 (2023): Julio-Diciembre; 357-366 |
dc.source.es-ES.fl_str_mv |
Revista de Investigación, Desarrollo e Innovación; Vol. 13 Núm. 2 (2023): Julio-Diciembre; 357-366 |
dc.source.none.fl_str_mv |
2389-9417 2027-8306 |
institution |
Universidad Pedagógica y Tecnológica de Colombia |
repository.name.fl_str_mv |
Repositorio Institucional UPTC |
repository.mail.fl_str_mv |
repositorio.uptc@uptc.edu.co |
_version_ |
1839633870503280640 |