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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

Deep Predictive Models for Collision Risk Assessment in Autonomous Driving

Deep Predictive Models of Furi Symposium Poster

The research objective is the implementation of a Collision Avoidance System for automobiles using deep neural networks. The researchers have been able to generate the data set in Webots, and use it to train/test the predictive model, thus obtaining higher levels of accuracy compared to the previous simulation environment (VREP). The next goal is to