EMG Gesture Recognition Algorithm Based on Parallel Multi-scale CNN

With the rapid development of science and technology, it has become a common concern of the society to provide more intelligent prosthetics for people with hand disabilities and help them better restore their ability to live. Electromyography, a weak potential difference signal that can be measured on the surface of human skin, has gradually become the preferred signal source for the control of bionic hand because of its simple acquisition and convenient processing. At this stage, the further development of EMG signal bionic hand is limited due to the poor stability, low recognition accuracy, few gesture categories, large algorithm scale and other reasons. In view of the above problems, through the analysis and processing of EMG signals, using digital filter, parallel multi-scale CNN network and other technologies, this paper establishes a set of EMG gesture recognition algorithm based on parallel multi-scale CNN. The experimental results show that the gesture recognition algorithm proposed in this paper has high accuracy and strong stability. It can classify more gesture categories and can be applied to the actual control of bionic hands. The algorithm has certain progressiveness., which provides more possibilities for the further development and application of intelligent hand prosthesis.