In order to enable patients speak without vocal cords, researchers at the University of California have developed a self-powered throat patch that translates muscle movements into speech using machine learning.
The concept for a non-invasive speech-enabling device was conceived by Jun Chen, an assistant professor of bioengineering at the University of California, Los Angeles, who noticed that his vocal cords were becoming fatigued from giving lectures for extended periods of time.
With the assistance of his colleagues at the University of California, he developed an inventive patch that adheres to the user’s throat and uses artificial intelligence (AI) to translate their muscle movements into voice. He started thinking about ways to enable people talk without using their vocal chords.
In addition to being sweat-resistant, the lightweight gadget uses the user’s muscle motions to create electricity, negating the need for a battery to run it.
Professor Jun Chen and his colleagues describe the operation of their 7.2-gram self-powering throat patch in a research that was published in the Nature Scientific Journal. Five thin layers make up the tiny device, some of which are constructed of materials that react to little movements of the throat muscles.
