The surface electromyography noise filtering and unwanted recordings attenuation for lower limb robotic system

Abstract

Exoskeleton robotic device (ERD) for rehabilitation purposes, physically interacts alongside with the user where high cognitive interaction and the safe human - machine system is required. To ensure safe interaction, there is a need to detect the user’s motion intention. One of the bio-signals that have been found to reflect directly the individual’s motion intention is surface electromyography (sEMG). However, sEMG signals are inevitably full of noises, not to mention the unwanted recordings and other artifact s between muscles where they cannot be freely used as a control signal for ERD. This paper presents the use of the Butterworth filter for noise suppression and the attenuation of unwanted recordings. Using classical Butterworth filter typically is unable to eliminate or attenuate the unwanted contamination on the signal of interest to its baseline level. Therefore, it is critical to modify the Butterworth filter at this stage. sEMG signals from the biceps femoris and rectus femoris muscles of seven health y male young adults were recorded in this study. The onset/ offset technique is utilized to detect the presence of the additional signal contaminated on the signal of interest. If the onset/offset index points are not approximately correlated with the movement, this means there is a contaminated measurement on the signal of interest. At this interval, a filter with distributed cutoff frequency plays the role to have the already smoothed baseline signal. In summary, the modified Butterworth filter shows to have a good performance to suppress the noises and to attenuate the unwanted recordings adaptively which ensures a safe human-machine system.