The effect of cut-off frequency when high-pass filtering equine sEMG signals during locomotion

Abstract

High-pass filtering (HPF) is a fundamental signal processing method for the attenuation of low-frequency noise contamination, namely baseline noise and movement artefact noise, in human surface electromyography (sEMG) research. Despite this, HPF is largely overlooked in equine sEMG research, with many studies not applying, or failing to describe, the application of HPF. An optimal HPF cut-off frequency maximally attenuates noise while minimally affecting sEMG signal power, but this has not been investigated for equine sEMG signals. The aim of this study was to determine the optimal cut-off frequency for attenuation of low-frequency noise in sEMG signals from the Triceps Brachii and Biceps Femoris of 20 horses during trot and canter. sEMG signals were HPF with cut-off frequencies ranging from 0 to 80 Hz and were subjected to power spectral analysis and enveloped using RMS to calculate spectral peaks, indicative of motion artefact, and signal loss, respectively. Processed signals consistently revealed a low-frequency peak between 0 and 20 Hz, which was associated with motion artefact. Across all muscles and gaits, a 30-40 Hz cut-off fully attenuated the low-frequency peak with the least amount of signal loss and was therefore considered optimal for attenuating low-frequency noise from the sEMG signals explored in this study.

Muscle Function and Kinematics during Submaximal Equine Jumping: What Can Objective Outcomes Tell Us about Athletic Performance Indicators?

“…A constant delay of 20 ms between kinematic and sEMG data was corrected for by shifting sEMG signals forward by 5 frames prior to further post-processing and analysis. sEMG signals from canter and jump trials were direct current (DC)-offset removed and high-pass filtered using a 4th order Butterworth filter with a 40 Hz cut-off frequency [38] and full wave rectified. The quality of each sEMG signal was visually scrutinized by two researchers (L.S.G., J.R.), prior to further signal processing and analysis.…”

“…The quality of each sEMG signal was visually scrutinized by two researchers (L.S.G., J.R.), prior to further signal processing and analysis. Signals were excluded from the dataset where visual signs of compromised sensor adhesion or inconsistent skin contact, due to the dynamic nature of the task, were apparent through high levels of baseline and movement artefact noise contamination, which were not attenuated by the appropriate high-pass filtering techniques applied [38]. Discrete sEMG variables included the timings of sEMG peak amplitude, activity onset, offset and the resultant activity duration for each muscle.…”

“…Unfortunately, a comparison of findings between these studies is confounded by methodological variation for sEMG signal detection and processing and differences in the jumping tasks studied. Further, guidelines on best practice for equine sEMG signal processing only appeared after these publications [32,38] and were therefore not employed by either of the studies. Kinematic data were also not collected, which is recommended for developing a comprehensive understanding of the role of equine musculature during jumping [39].…”

Winter School on sEMG Signal Processing: An Initiative to Reduce Educational Gaps and to Promote the Engagement of Physiotherapists and Movement Scientists With Science

“…Considering the topics addressed in the school, a good amount of time was spent on explaining filtering, especially because filtering is a fundamental step in the sEMG data analysis because of its susceptibility to low-frequency noise, baseline noise, and movement artifact noise ( 10 , 27 ). Filtering was discussed considering pieces of evidence from the literature concerning the most adequate cut off frequencies, filter design, and the criteria for its determination ( 33 ). With this approach, we also wanted to reduce the barriers that students face when reading scientific papers and not properly understanding why and how the sEMG signals were processed.…”

Scientific athletics training: flexible sensors and wearable devices forkineses monitoring applications

“…表面肌电信号采集的方式通常需要 表面电极、 肌电采集系统、 高速摄像机等设备. 目前， 商业的肌电采集系统主要有美国 Noraxon、 Delsys [46] 、 德国 FreeEMG 等系统. 随着无线通讯技术的发展， 肌电信号采集系统可以实现 100 米范围内的无线传输数 据.…”