FURI | Spring 2018
A Deep Learning Autoencoder for EMG Changepoint Recognition in Robotic Applications
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.
Computer systems engineering
Hometown: Tempe, Arizona
Graduation date: Fall 2020