High accuracy recognition of muscle fatigue based on sEMG multifractal and LSTM
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College of Mechatronics and Automobile Engineering, Chongqing Jiaotong University, Nanan District, Chongqing, China
Submission date: 2023-06-06
Final revision date: 2023-10-10
Acceptance date: 2023-11-06
Online publication date: 2024-01-24
Publication date: 2024-01-31
Corresponding author
Xia Zhang   

College of Mechatronics and Automobile Engineering, Chongqing Jiaotong University, China
Journal of Theoretical and Applied Mechanics 2024;62(1):117-128
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 prop- erties 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.
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