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Elliot Nester

Hometown: Tempe, Arizona | Graduation Date: Fall 2020
Computer Systems Engineering

A Deep Learning Autoencoder for EMG Changepoint Recognition in Robotic Applications

Mentor: Heni Ben Amor
FURI: Spring 2018

Electromyography (EMG) sensors can be added to a variety of devices for detecting nerve signals in muscles, but there is a need for better signal analysis techniques and more flexible models for interpreting gestures. Using a machine learning technique called deep learning, the researcher developed a model for detecting gesture changes in EMG data with a high level of accuracy. This kind of model could be used for allowing users to control prosthetics or assistive exoskeletons with a high degree of precision. Future work should be done to develop even better models and make these medical devices more effective.