A Multifeatured Time–Frequency Neural Network System for Classifying sEMG

Human gesture-related electromyographic signal in intelligent recognition systems has attracted widespread attention. The EMG signal is the temporal and spatial superposition of motor unit neural potentials and it contains nonlinear and complex characteristics. Despite the previous efforts, recognizing more different actions and building an online recognition system remain challenging. To address these issues, we construct a multi-feature time-frequency neural network system and a dataset which contains twenty kinds of hand movements. In a multi-featured time-frequency neural network system, the multi-layer CNN structure is used to obtain faster recognition and better classification results. The experimental results demonstrate that equipment and algorithmic model can reach a 94.66% accuracy. Our approach focuses on processing multiple time and frequency domain features of sEMG using CNN. Importantly, we also perform real-time validation. The system can accurately recognize hand movements to control UAV motion in real-time, in which case system classification takes 0.05s. This demonstrates the practicality and effectiveness of the sEMG acquisition system based on the multifeatured time-frequency neural network.