Artifact removal from sEMG signals recorded during fully unsupervised daily activities.

Abstract

In this study, we propose a method for removing artifacts from superficial electromyography (sEMG) data, which have been widely proposed for health monitoring because they encompass the basic neuromuscular processes underlying human motion. Our method is based on a spectral source decomposition from single-channel data using a non-negative matrix factorization. The algorithm is validated with two data sets: the first contained muscle activity coupled to artificially generated noises and the second comprised signals recorded under fully unsupervised conditions. Algorithm performance was further assessed by comparison with other state-of-the-art approaches for noise removal using a single channel. The comparison of methods shows that the proposed algorithm achieves the highest performance on the noise-removal process in terms of signal-to-noise ratio reconstruction, root means square error, and correlation coefficient with the original muscle activity. Moreover, the spectral distribution of the extracted sources shows high correlation with the noise sources traditionally associated to sEMG recordings. This research shows the ability of spectral source separation to detect and remove noise sources coupled to sEMG signals recorded during unsupervised daily activities which opens the door to the implementation of sEMG recording during daily activities for motor and health monitoring.