Ensenas Chishandiso cheKukurukurirana naSaeli Seira Saenz and Alana Betanzo Carriles

Dhizaini Ensenas Chishandiso cheKukurukurirana naSaeli Seira Saenz and Alana Betanzo Carriles ndiye mukundi anokudzwa zvikuru wemubairo unoremekedza we2023 Mubairo weSimbi we A' Design Award muChikwata cheKukwikwidza cheDhizaini yeZveVanhu.

SimbiZveVanhu· 2023

Ensenas Chishandiso cheKukurukurirana

na Saeli Seira Saenz and Alana Betanzo Carriles

Mutengi: Universidad de Monterrey

Mukundi weMubairo weSimbi we A' Design Award muChikwata cheZveVanhu

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Nezvekwa Ensenas Chishandiso cheKukurukurirana

Ensenas ndiro dhijitari rakagadzirwa kuti rivandudze kukurukurirana pakati pevanhu vane chirema chekunzwa nevanhu vanonzwa muMexico. Ensenas inoshandisa modheri yemachine learning inoverenga Mexican Sign Language munguva chaiyo uye inoshandura mazwi kuita Spanish yakanyorwa. Modheri iyi yakadzidziswa nezvinyorwa zvine mavhidhiyo mazana mana (400) ezviratidzo zvinowanzoshandiswa, zvakanyorwa uye zvakaongororwa firemu neframu kuburikidza nepose estimation. Webhusaiti inoshanda pamidziyo yakasiyana inoratidza purojekiti iyi uye inopa ongororo, modheri, uye zvinyorwa zvekutora pasina mari. Chinangwa ndechekuderedza mukaha wekukurukurirana.

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Nezve Mubairo weSimbi we A' Design Award

Mubairo weSimbi we A' Design Award wakahwinwa ne Ensenas Chishandiso cheKukurukurirana, rukudzo runokudzwa runopiwa kumadhizaini akanaka anozadzisa zvinodiwa zvehunyanzvi neyeindasitiri zvinotarisirwa kuzvigadzirwa zvakagadzirwa zvakanaka. Vakunda veSimbi vanoratidza unyanzvi hukuru, hunyanzvi hwakakwirira, kunzwisisa kwevakachenjera uye kugona kwekugadzira kwevagadziri vavo. Ensenas Chishandiso cheKukurukurirana inotarisirwa kuva chigadzirwa chinokudzwa zvakanyanya, chinoremekedzwa uye chinodiwa, chinoshanda uye chine zvitsva. Dhizaini iyi yakahwina Mubairo weSimbi we A' Design Award inosanganisa maitiro akanakisa eindasitiri uye ine hunhu hwetekinoroji hwakakwana hwakabatana neruzivo nehunyanzvi. Nekushanda kwayo zvakanaka, Ensenas Chishandiso cheKukurukurirana inoremekedzwa uye yakagadzirirwa kupa kuvandudzwa kwehupenyu kunobatsira kuita kuti nyika ive nzvimbo iri nani.

Shure kweDhizaini

Bepa Renhau

Zvikwereti, nhengo dzetiimu nezvimwe zvakadzama zvakapihwa nemudhizaina.

Hunhu Hwakasiyana: 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.
Kukura & Kupakiwa: This is a model in a website, whit a responsive design.
Matagi: IA, Machine Learning, LSM, Data base, Service Design, education and tools, Sign Language,
Tekinoroji Yekugadzira: 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.
Changamire yeDhizaini: 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.
Tsvakurudzo: 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.
Chikurudziro: 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.
Nguva yePurojekiti: 6 months on Universidad de Montery and Monterrey City in México
Nhengo dzeTimu: Saeli Seira Sáenz
Alana Betanzo Carriles