Real‐time and user‐independent feature classification of forearm using EMG signals

Electromyography (EMG) signals contain various information about human motion. How to extract the EMG signals of the human body by appropriate methods for classification is a hot issue in current research. Unfortunately, the main problem with the classification of EMG signals is that only certain actions can be identified. Once the individual is changed, the recognition accuracy rate will be greatly reduced. This study introduces a method for classifying the forearm using back propagation (BP) neural networks. This mode extracted five features of the EMG signals. Participants were required to train their own actions during the test. Six participants selected four to six actions to identify them, and the average accuracy was more than 90%. The results suggest that the method can be used among different individuals and provides a good classification method. This study introduces a method for classifying the forearm using back propagation (BP) neural networks. Six participants selected four to six actions according to their willingness, and the average accuracy was more than 90%. The results suggest that the method can be used among different individuals and provides a good classification method.