Saeli Seira Saenz and Alana Betanzo Carriles의 Ensenas 커뮤니케이션 도구

디자이너 Saeli Seira Saenz and Alana Betanzo Carriles의 작품 Ensenas 커뮤니케이션 도구은(는) 매우 권위 있는 2023 A' Design Award 철상의 소셜 디자인 부문에서 탁월한 수상작입니다.

철소셜· 2023

Ensenas 커뮤니케이션 도구

디자이너: Saeli Seira Saenz and Alana Betanzo Carriles

클라이언트: Universidad de Monterrey

소셜 부문 A' Design Award 철상 수상작

35,348

인증된 수상작

#149366 수상 상태 인증하기

사이트 방문 A' Design Award에서 보기 보도 자료 수상 배지

색상

디자인

Ensenas 커뮤니케이션 도구 소개

Ensenas는 멕시코에서 청각 장애인과 청인 간의 소통을 개선하기 위해 개발된 디지털 도구이다. Ensenas는 멕시코 수어를 실시간으로 읽어 단어를 문자 스페인어로 번역하는 머신러닝 모델을 활용한다. 이 모델은 자주 쓰이는 수어 동작을 담은 400개의 영상 데이터 세트로 학습되었으며, 각 영상은 라벨링을 거쳐 자세 추정(pose estimation) 기법을 통해 프레임 단위로 분석되었다. 반응형 웹사이트를 통해 프로젝트를 소개하고, 연구 자료와 모델, 데이터 세트를 무료로 내려받을 수 있도록 제공한다. 궁극적인 목표는 소통의 간극을 줄이는 것이다.

수상 인정

A' Design Award 철상 소개

Ensenas 커뮤니케이션 도구, 이 작품은 A' Design Award 철상 수상작으로, 잘 디자인된 창작물에 기대되는 전문적·산업적 요건을 충족하는 좋은 디자인에 수여되는 권위 있는 인정입니다. 철상 수상작은 창작자의 뛰어난 기술, 높은 전문성, 전문가다운 이해, 창의적 역량을 보여 줍니다. Ensenas 커뮤니케이션 도구, 이 작품은 높이 평가받고 감탄과 사랑을 받는 창작물로서 실용적이고 혁신적일 것으로 기대됩니다. 이 철상 수상 디자인은 업계 모범 사례를 통합하며 노하우와 재능으로 연결된 유능한 기술적 특성을 갖추고 있습니다. Ensenas 커뮤니케이션 도구, 이 작품은 철저함으로 존중받으며 삶의 질을 개선하여 세상을 더 나은 곳으로 만들도록 디자인되었습니다.

디자인 비하인드

프레스 킷

디자이너가 제공한 크레딧, 팀원 및 기술 세부 사항.

고유 특성: 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.
치수 및 패키징: This is a model in a website, whit a responsive design.
태그: IA, Machine Learning, LSM, Data base, Service Design, education and tools, Sign Language,
생산 기술: 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.
디자인 과제: 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.
연구: 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.
영감: 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.
프로젝트 기간: 6 months on Universidad de Montery and Monterrey City in México
팀원: Saeli Seira Sáenz
Alana Betanzo Carriles