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  /  György Lévay

György Lévay

György Lévay is a Research Engineering Consultant for upper limb prostheses and Research Manager at Infinite Biomedical Technologies, a US company specializing in upper limb prosthesis accessories.
Originally from Hungary, Lévay completed his Master’s Degree in Biomedical Engineering on a Fulbright scholarship at John Hopkins University. There he conducted research in machine learning-based control algorithms and digital signal processing strategies for upper-limb prostheses.

In 2015, Lévay led a design team that won the Grand Prize in the Intel-Cornell Cup with GEAR (Game-Enhancing Augmented Reality), an assistive device that allows people with upper-limb deficiencies to control computers with their feet. The idea behind the device came from Lévay, a passionate gamer, who lost his hands to a meningitis infection, and therefore lost the ability to play computer games.

Lévay is committed to presenting the state of technology for upper limb prostheses in an easily digestible way, highlighting both the advancements and limitations. He believes that as speech recognition began as an assistive tool for the visually impaired and become a part of everyday life, prosthesis research is a foundation of organic human-machine interfacing for all of us in the not-so-distant future.

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All Sessions By György Lévay

A step towards singularity: connecting brain, body and machine in the name rehabilitation
Masters&Robots Day 2
György Lévay suffered a devastating meningitis infection in 2010 which forced his doctors to amputate from both his arms and feet. After receiving his first upper-limb prosthesis a year later he realized that there was a significant difference between what these devices were said to be capable of, and what they were actually capable of. Already studying to be an engineer, he shifted his field of interest to the control systems of upper-limb prostheses, and after being awarded a Fulbright scholarship he completed his Master’s in Biomedical Engineering at The Johns Hopkins University in the United States. There, he focused on pattern recognition, a machine learning method used for intuitive control of multi-articulated prosthetic limbs. At Masters & Robots he will show how and why these control systems work, what it takes to teach them the individual traits of their users, and through a demonstration with the help of an audience member everyone will see first hand both the exciting and frustrating aspects of using these devices.