Saeli Seira Saenz and Alana Betanzo Carriles nisqap Ensenas Rimanakuy Herramienta diseñon

Saeli Seira Saenz and Alana Betanzo Carriles nisqap Ensenas Rimanakuy Herramienta diseñonqa Ayllu Kamay Atipanakuy T'aqapi 2023 A' Design Award Fierru Premiu nisqap ancha chaniyuq atipaqninmi.

FierruAyllu· 2023

Ensenas Rimanakuy Herramienta

Saeli Seira Saenz and Alana Betanzo Carriles rurasqa

Mañakuq: Universidad de Monterrey

Ayllu T'aqapi A' Design Award Fierru Premiu Atipaq

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Taripasqa Premio Atipaq

#149366 nisqapaq Premio Kayninta Taripay

Sitio Web Qhaway A' Design Award ukhupi Qhaway Prensa Willakuy Premio Unanchay

Llinphikuna

Diseñomanta

Ensenas Rimanakuy Herramienta nisqamanta

Ensenas nisqaqa huk digital herramientam, uyarinapi sasachakuyniyuq runakunapa hinaspa uyarikuq runakunapa rimanakuyninta allinchanapaq ruwasqa, Mexico llaqtapi. Ensenas nisqaqa "machine learning" nisqa yachachiy modeloman churakun, chaymi Mexico llaqtapi Rimanakuy Makikunawan (Lengua de Señas Mexicana) qhawarispa, kunanpi kunallanpi, castellano qillqasqaman tikrachin. Kay modelotaqa 400 videokunawan yachachichirqaku, kay videokunapi rikuchikuq makikunawan rimayninkunata "pose estimation" nisqawan sapa frametam qhawarispa, sutinchaspa, t'aqwirispa yachachirqaku. Huk website, imaymana llamk'aq (responsive) kaqmi, kay proyectota rikuchin, hinallataq investigacionta, modelota, hinaspa datos huñusqata mana qullqiwan qhichunapaq quwan. Kay llamk'ayqa rimanakuypi tiyaq t'uqyaykunata (pisichanapaq) aswan pisiyachinanpaq ruwakusqa.

Riqsichiy

A' Design Award Fierru Premiu nisqamanta

Ensenas Rimanakuy Herramienta A' Design Award Fierru Premiu mishak kan, yupaychay, kayta ruray yachak shinallatak industriapak mañaykunata paktachik alli diseñukunaman kun. Fierru mishak kashkakunaka hatun ruray ushayta, hanak yachayta, yachak yuyayta shinallatak ruray kamachiyta rikuchinku. Ensenas Rimanakuy Herramienta sinchi yupaychashka, kuyashka shinallatak kuyashka ruray, ruranapak alli shinallatak mushuk yuyaypi kanata munanku. Kay Fierru Premiu japishka diseñuka industriapak ashtawan alli ruraykunata tantachin shinallatak yachaywan shinallatak ushaywan tantashka tekniku ushayta charin. Sinchi ruraymanta Ensenas Rimanakuy Herramienta yupaychashka kan shinallatak kawsayta alliyachik ruraykunata kuypak ruray kan, pachata ashtawan alli kachun ruran.

Diseño Qhipanpi

Prensa Kit

Diseñadorpa quska créditos, llamk'aq runakunapas técnico willaykuna.

Sapaq Kayninkunamanta: Likewise, thanks to technology and current tools, we were able to generate a Machine Learning model or tool which makes a real time reading of the Mexican Sign Language; where it is possible to solve the communication gap between people with hearing disabilities and people without such disability; avoiding that people make the effort to learn the Mexican Sign Language.
Tupukuna & P'istuy: This is a model in a website, whit a responsive design.
Etiquetakuna: IA, Machine Learning, LSM, Data base, Service Design, education and tools, Sign Language,
Ruway Tecnología: We developed a model and data set of machine learning in order to create new tools and opportunities for people with hearing disabilities and make their lives easier through it. We created a platform in which anyone can learn more about the project and download the machine learning model.
Diseño Sasachakuy: The first step in creating this project was the creation of a data set consisting of a collection of 400 videos of the most used words in the Mexican sign language. These were ordered by categorize and label individually.
The next step was the pose prediction process, in which the model analyzes the content of the data set analyzing frame by frame by means of ML pipe pose Estimation.
At the end of this estimation, our supervised learning model is able to detect patterns in the poses without the need for human intervention in order to identify which words, letters, numbers and phrases they belong to. To complete this process, a manual selection of frames was also performed in order to achieve a more accurate prediction of the signals.

In order to open this project to the public, a digital platform was created in which it is possible to freely download our research, the LSM recognition model, the data set generated so that other developers, technology companies, educational institutions can take advantage of our project and can create more inclusion tools for the non-hearing community.
T'aqwiy: It is estimated that 5% of the world population, being 430 million people, will suffer a loss of disabling audio and in Mexico there are limited resources and tools to create an environment where everyone feels heard.
Following a design process, we conducted a series of interviews with experts, case studies and surveys to understand the perception of hearing impairment. We found that one of the limitations for the use of sign language is the learning curve.

We also found that some basic tasks for people with this disability can be more complicated than normal, which limits their independence. That's why we wanted to find a solution
inclusion to this problem.

This is Enseñas, an initiative that seeks inclusion through a digital tool made by through a machine learning model (LSM Model) with the purpose of creating a new way of
communication that facilitates the interaction between people with hearing disabilities and hearing people in real times. This model will be able to translate words from the Mexican sign language into Spanish written and vice versa in real time.
Kallpachay: The lack of education and tools about Mexican Sign Language to improve the communication and experience of hearing impaired people in Mexico, has led us to create awareness and promote the inclusion of this problem through a digital platform.
Llamk'ay Pacha: 6 months on Universidad de Montery and Monterrey City in México
Huñu Runakuna: Saeli Seira Sáenz
Alana Betanzo Carriles