Effects of sampling rate on automated fatigue recognition in surface EMG signals

Abstract

This study investigated the effects different sampling rates may produce on the quality of muscle fatigue detection algorithms. sEMG signals were obtained from isometric contractions of the arm. Subsampled signals resulting in technically relevant sampling rates were computationally deduced from the original recordings. The spectral based fatigue recognition methods mean and median frequency as well as spectral moment ratio were included in this investigation, as well as the sample and the fuzzy approximate entropy. The resulting fatigue indices were evaluated with respect to noise and separability of different load levels. We concluded that the spectral moment ratio provides the best results in fatigue detection over a wide range of sampling rates.

Keywords

• emg
• muscle fatigue
• sample rate
• mnf
• mdf
• spectral moment ratio
• sample entropy
• fuzzy approximate entropy
• fes
• physiotherapy
• neuroprosthesis