疲劳分析matlab代码.zip

In this project, differential sEMG recordings were collected from three subjects during isometric biceps muscle contraction under two different loading conditions (3 Kg and 5.5 Kg) to address the nonlinear dynamics of the sEMG signals. Signal processing techniques included: 1. Fast Fourier Transform 2. Filtration 3. Rectification 4. Normalization Then some nonlinear tools were utilized including: Chaos, Fractal and Entropy to characterize the processed sEMG signals. The chaotic behavior of the signal was determined by the Largest Lyapunov Exponent (LLE), fractals were computed using the Box Counting method (BCFD) and entropy was identified by the Approximate Entropy (ApEn).