Abstract
This article presents the results of the classification of measurement errors of electromyography signals caused by interference and noise. It is shown that interference and measurement noise mainly occur during the registration of electromyography signals and significantly reduce the measurement accuracy. To classify measurement errors, the article analyzes and identifies the features of interference and measurement noise. This analysis is based on the mechanism of occurrence and the physical nature of interference and noise, as well as their spectral, frequency, and statistical characteristics. Initially, to analyze and identify the features of interference and noise, the article examines the characteristics of electromyography signals recorded during the movement of a human limb. It is shown that the informative components of the motor units of electromyography signals during limb movement are variable. The analysis results revealed that measurement noise and interference are slowly variable and have a Gaussian distribution. The frequency composition of low- and high-frequency interference and measurement noise is distinct from that of the measured electromyography signals. To systematize the results of the analysis, a classification scheme for measuring errors in electromyography signals is presented. It is recommended to use the measurement error classification scheme when solving the problem of signal filtering, where it is necessary to identify informative components for measuring and evaluating the movement of human limbs using electromyography signals. Furthermore, a comparative evaluation of band-pass filters of different orders was conducted to eliminate interference. A lower-order band-pass filter, even when implemented unidirectionally, improves the signal-to-noise ratio and highlights informative components.