Ensenas Chida cholankhulirana ndi Saeli Seira Saenz and Alana Betanzo Carriles

Dizayini ya Ensenas Chida cholankhulirana ndi Saeli Seira Saenz and Alana Betanzo Carriles ndi wopambana wotamandika kwambiri pa mphotho yaulemu ya 2023 Mphoto ya Chitsulo ya A' Design Award mu Gawo la Mpikisano la Mapangidwe a Zachikhalidwe.

ChitsuloZachikhalidwe· 2023

Ensenas Chida cholankhulirana

ndi Saeli Seira Saenz and Alana Betanzo Carriles

Kasitomala: Universidad de Monterrey

Wopambana pa Mphoto ya Chitsulo ya A' Design Award mu Gawo la Zachikhalidwe

35,348

Wopambana Wotsimikizidwa

Tsimikizani Mkhalidwe wa Mphotho ya #149366

Pitani ku Tsamba Onani ku A' Design Award Nkhani Yofalitsidwa Chizindikiro cha Mphotho

Mitundu

Kapangidwe

Za Ensenas Chida cholankhulirana

Ensenas ndi chida cha digito chopangidwa kuti chithandize kulumikizana pakati pa anthu omva movutikira ndi anthu omva bwino ku Mexico. Ensenas amagwiritsa ntchito njira ya machine learning yomwe imawerenga Chinenero cha Zizindikiro cha Mexico (Mexican Sign Language) mwanthawi yeniyeni ndikumasulira mawuwo kukhala Chisipanishi cholembedwa. Njirayi inaphunzitsidwa pogwiritsa ntchito deta ya mavidiyo 400 a zizindikiro wamba, zomwe zinasankhidwa ndi kusanthulidwa mfelemu ndi felemu kudzera mu njira yotchedwa pose estimation. Webusayiti yosinthasintha malinga ndi chipangizo imafotokoza za ntchitoyi ndipo imapereka mwaulere kafukufukuyu, njira yogwiritsira ntchito, ndiponso deta yonse kuti anthu azitha kuyitsitsa. Cholinga chake ndi kuchepetsa mpata wolumikizirana pakati pa magulu awiriwa.

Chidziwitso

Za Mphoto ya Chitsulo ya A' Design Award

Ensenas Chida cholankhulirana ndi mmodzi mwa opambana a Mphoto ya Chitsulo ya A' Design Award, kuzindikirika kolemekezeka komwe kumaperekedwa kwa mapangidwe abwino omwe amakwaniritsa zofunika za akatswiri ndi za makampani zomwe zimayembekezeka kuchokera ku zolengedwa zopangidwa bwino. Opambana a Chitsulo amaonetsa luso lalikulu, ukatswiri wapamwamba, kumvetsetsa kwa akatswiri ndi mphamvu yolenga ya opanga awo. Ensenas Chida cholankhulirana akuyembekezeka kukhala cholengedwa choyamikiridwa kwambiri, chosirira ndi chokondedwa chomwe ndi chothandiza ndi chatsopano. Mapangidwe opambana Mphoto ya Chitsulo ya A' Design Award awa amaphatikiza machitidwe abwino kwambiri a makampani ndipo ali ndi makhalidwe a ukadaulo oyenerera olumikizidwa ndi chidziwitso ndi luso. Ensenas Chida cholankhulirana amalemekezedwa chifukwa cha kusamala kwake ndipo anapangidwa kuti apereke kukonza kwa moyo kumene kumathandiza kusandutsa dziko kukhala malo abwino.

Kumbuyo kwa Kapangidwe

Katundu wa Atolankhani

Umboni, mamembala a gulu ndi zambiri zapadera zoperekedwa ndi wopanga.

Makhalidwe Apadera: 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.
Miyezo ndi Kuphimba: This is a model in a website, whit a responsive design.
Zizindikiro: IA, Machine Learning, LSM, Data base, Service Design, education and tools, Sign Language,
Teknoloji Yopangira: 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.
Vuto la Dizayini: 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.
Kafukufuku: 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.
Chilimbikitso: 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.
Nthawi ya Polojekiti: 6 months on Universidad de Montery and Monterrey City in México
Mamembala a Gulu: Saeli Seira Sáenz
Alana Betanzo Carriles