Saeli Seira Saenz and Alana Betanzo Carriles 嘅作品 Ensenas 沟通工具

由設計師 Saeli Seira Saenz and Alana Betanzo Carriles 創作嘅 Ensenas 沟通工具係備受推崇嘅 2023 A' Design Award 鐵獎得獎作品，獲獎類別為社會設計。

鐵社會· 2023

Ensenas 沟通工具

由 Saeli Seira Saenz and Alana Betanzo Carriles

客戶: Universidad de Monterrey

社會類別嘅 A' Design Award 鐵獎得獎者

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經驗證嘅獲獎者

驗證設計 #149366 嘅獲獎狀態

訪問網站喺 A' Design Award 查看新聞稿獎項徽章

色彩

設計

關於 Ensenas 沟通工具

Ensenas係一個數碼工具,專門為改善墨西哥聽障人士同健聽人士之间嘅沟通而設計。Ensenas運用機器學習模型,實時讀取墨西哥手語,並將手語翻譯做書面西班牙文。呢個模型係用一套包含400條常用手語影片嘅數據集訓練出嚟,通過姿態估計技術逐幀標註同分析。項目仲設有一個響應式網站,介紹呢個計劃,並提供研究成果、模型同數據集免費下載。目標係縮窄溝通嘅隔閡。

榮譽認可

關於A' Design Award 鐵獎

Ensenas 沟通工具是 A' Design Award 鐵獎得主，這是授予滿足人們對設計精良的創作所期待的專業與工業要求的優良設計的聲望認可。鐵獎得主展現出其創作者的高超技藝、高度專業化、專家級的理解力和創造能力。預計Ensenas 沟通工具將成為一件備受推崇、令人欽佩且被珍愛的創作，既實用又創新。這件贏得 A' Design Award 鐵獎的設計整合了行業最佳實踐，並擁有與專業知識和才華相結合的稱職技術特性。Ensenas 沟通工具因其嚴謹而受到尊重，旨在提供有助於讓世界變得更美好的生活品質改善。

設計背後

新聞資料包

由設計師提供嘅製作人員名單、團隊成員同技術詳情。

獨特屬性: 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.
尺寸同包裝: This is a model in a website, whit a responsive design.
標籤: IA, Machine Learning, LSM, Data base, Service Design, education and tools, Sign Language,
生產技術: 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.
設計挑戰: 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.
研究: 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.
靈感: 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.
項目時長: 6 months on Universidad de Montery and Monterrey City in México
團隊成員: Saeli Seira Sáenz
Alana Betanzo Carriles