In powered lower limb prostheses and exoskeleton control, sEMG based methods have been widely explored and utilized due to non-invasive nature and ability to directly reflect user intentions. However, in practical use, neuromuscular fatigue (NMF) caused by long-term use can cause significant changes in sEMG signals. These issues are constraining sEMG's active prosthetics from the laboratory to real life. In this study, the changes in sEMG signals with muscle fatigue was investigated. sEMG data from one healthy subject and a below-knee amputee were logged and analyzed when they walked on a treadmill for a long time. They will experience from no NMF to NMF and then adapt to no NMF. A variational mode decomposition-based soft interval threshold (VMD-SIT) method was used to denoise the sEMG signals. Feature extraction methods were used to extract the features of the sEMG signal, and the distribution changes of these features during walking were analyzed. The results indicated that the NMF of the amputee was quite different from and more variable than that of the healthy subject, and the ratios of features' mean value of amputee and healthy are much higher in residual-limb side (12.35-26.18) than normal side (3.52-5.91).
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