Use of Deep Machine Learning Methods of Artificial Neural Networks for Designing Algorithms of Electromyography Signal Recognition in Bionic Prosthesis

For sterling application of scientific and engineered achievements in field of bionic prosthesis it’s required to provide comfortable and natural human‑prosthesis interface for an end‑user. In this article we are looking into ways and methods of analysis of the signal collected through electromyography activity of muscles on the skin surface. Such signal is nonstationary and unstable by its nature, dependent on various factors. sEMG based interface has several unsolved problem at the moment, such as insufficient accuracy of recognition and noticeable delay caused by signal recognition and processing. Article is dedicated to application of deep machine learning required to provide decent recognition of electromyography signals. In the course of the research hardware was developed to register muscle activity. Data collecting system and algorithms of gesture recognition have been designed as well. In conclusion decent results were achieved by using convolutional neural network, with two‑dimensional input, since data stream has obvious translational orientation. In the future, modification of neural network architecture, learning algorithms and experiments with structure of data are planned.