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

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.