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Girls create translation armband for the deaf

Shuyue Zhang 于 2018-01-30 15:02 分享

About the solution

Inspired by a deaf friend, Nana Wang and her friend Shuang Huang started developing a solution for people with hearing disorders 2015. After two years of hard work and thousands of experimentations, they successfully developed a gesture recognition armband which recognize the most common 400 gestures from sign language.

Each gesture of the sign language sends different electrical muscle signals. The armband developed by Nana and Shuang collects those signals, analyse through AI deep learning and finally convert them into speech. “It is extremely difficult to accurately recognize a gesture. Usually it requires to collect the signal for a thousand, even several thousand times.” explained the inventors.

"My friend, who is a deaf, told me he once sent a resume and then someone called him. However, he couldn’t answer the phone nor speak anything”, said Nana Wang, a student from Beijing University of Aeronautics and Astronautics.

"In China, there are 70 million people with language dysfunction, for a variety of reasons”, said Shuang Huang, Nana Wang’s friend and roommate in the university, a student from Tsinghua University.

Although currently, the armband can only translate common gestures one by one, the students believe that they will be able to improve it with the fast development of AI. The sign language recognition armband has also won the championship in an innovative competition held by the United Nations and an Internet company. The girls said they are not thinking of making money out of their invention yet, but would be pleased to help more deaf people communicate normally.

Adapted from: https://bit.ly/2MRv2dk
https://bit.ly/2M5hJGU

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关于发明者

Nana Wang, born in 1994, and Shuang Huang, born in 1995, in China, are postgraduate students from top universities in China. To help Wang’s friend who suffers from hearing loss and other deaf people, they developed a translation armband, which can convert sign language into speech.

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