High accuracy recognition of muscle fatigue based on semg multifractal and lstm

A muscle fatigue identification method that integrates the multifractal of sEMG with LSTM is proposed. The MFDMA method was introduced to analyze and extract non-linear properties of sEMG. The significance of differences between the fatigue and non-fatigue states in terms of spectral width, Hurst index variation difference, and peak singularity index was determined using the t-test. A LSTM networks under the combined feature set comprising multiple fractals was built, and its recognition accuracy was 98.91%. The LSTM network model was found to be more accurate than other classification methods in identifying muscle fatigue under the same feature set.