Fault-Tolerant Sensor Detection of sEMG signals: Quality Analysis Using a Two-Class Support Vector Machine.

Abstract

The capacity to identify the contamination in surface electromyography (sEMG) signals is necessary for applying the sEMG controlled prosthesis over time. In this paper, the method for the automatic identification of commonly occurring contaminant types in sEMG signals is evaluated. The presented approach uses two-class support vector machine (SVM) trained with clean sEMG and artificially contaminated sEMG. The contaminants considered include electrocardiogram interference, motion artefact, power line interference, amplifier saturation, and electrode displacement. The results demonstrated that the sEMG signal with the contaminants could readily be distinguished, even with increase channels degraded. The SFTD detection depends on the noise type, whether the amputee or non-amputee subjects and which channel is being analysed. This method presented a suitable solution for the detection of contaminants in the sEMG signal, being able to provide the acquired signal validation before the movement intended recognition to operate in an intelligent recognition with greater reliability.