ALS Text-To-Speech

ALS Text-To-Speech

Penn Adapt

Created an Arduino driven text to speech device for ALS patients. A variety of single input mechanisms were created to interface with the project, making it suitable for various stages of the progression of the disease. Inputs included an ability switch, finger oscillation, cheek-twitching and muscle flexure. Patients would be able to use any input to control our on screen software to "type" words, which is then eventually converted to speech. A demonstration can be seen above. The product was selected to compete in Rothberg Catalyzer at Penn Competition in 2018. 

Arjun Dave, Mechanical Engineering, University of Pennsylvania, 2020

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