This work introduces a method for high-accuracy EMG based gesture identification. A newly developed deep learning method, namely, deep residual shrinkage network is applied to perform gesture identification. Based on the feature of EMG signal resulting from gestures, optimizations are made to improve the identification accuracy. Finally, three different algorithms are applied to compare the accuracy of EMG signal recognition with that of DRSN. The result shows that DRSN excel traditional neural networks in terms of EMG recognition accuracy. This paper provides a reliable way to classify EMG signals, as well as exploring possible applications of DRSN.

In this Arduno Machine learning project we're going to use an accelerometer sensor to identify the gestures you play. This is a remake of the project found on the Tensorflow blog. We're going to use a lot less powerful chip in this tutorial, tough: an Arduino Nano (old generation), equipped with 32 kb of flash and only 2 kb of RAM. We're going to use the accelerations along the 3 axis (X, Y, Z) coming from an IMU to infer which gesture we're playing. We'll use a fixed number of recordings (NUM_SAMPLES) starting from the first detection of movement.