EMG-force relationship during static contraction: Effects on sensor placement locations on biceps brachii muscle

Abstract

BACKGROUND: The relationship between surface electromyography (EMG) and force have been the subject of ongoing investigations and remain a subject of controversy. Even under static conditions, the relationships at different sensor placement locations in the biceps brachii (BB) muscle are complex. OBJECTIVE: The aim of this study was to compare the activity and relationship between surface EMG and static force from the BB muscle in terms of three sensor placement locations. METHODS: Twenty-one right hand dominant male subjects (age 25.3 ± 1.2 years) participated in the study. Surface EMG signals were detected from the subject's right BB muscle. The muscle activation during force was determined as the root mean square (RMS) electromyographic signal normalized to the peak RMS EMG signal of isometric contraction for 10 s. The statistical analysis included linear regression to examine the relationship between EMG amplitude and force of contraction [40-100% of maximal voluntary contraction (MVC)], repeated measures ANOVA to assess differences among the sensor placement locations, and coefficient of variation (CoV) for muscle activity variation. RESULTS: The results demonstrated that when the sensor was placed on the muscle belly, the linear slope coefficient was significantly greater for EMG versus force testing (r 2 = 0.62, P < 0.05) than when placed on the lower part (r 2 = 0.31, P > 0.05) and upper part of the muscle belly (r 2 = 0.29, P < 0.05). In addition, the EMG signal activity on the muscle belly had less variability than the upper and lower parts (8.55% vs. 15.12% and 12.86%, respectively). CONCLUSION: These findings indicate the importance of applying the surface EMG sensor at the appropriate locations that follow muscle fiber orientation of the BB muscle during static contraction. As a result, EMG signals of three different placements may help to understand the difference in the amplitude of the signals due to placement.

An examination of motor unit firing rates during steady torque of maximal efforts with either an explosive or slower rate of torque development

“…(2018, 2019) examined the leg extensors. The differences between the findings are possibly attributable to the fact that the EMG–torque relationship can vary depending on the muscles analysed (Ahamed et al., 2014; Alkner et al., 2000). In addition, there are differences in the calculations of torque and EMG AMP between the present study and previous investigations.…”

Effects of static exercises on hip muscle fatigue and knee wobble assessed by surface electromyography and inertial measurement unit data

“…We did not incorporate a limb-specific analysis to measure imbalances prior to the experiment or during the experiment for each participant since we only analyzed one limb, however expanding our approach to include both limbs could be employed in future work. While sEMG is commonly used to measure muscle activity and estimate characteristics such as muscle fatigue and activation timing, the optimal procedure for analyzing the raw signal to estimate muscle fatigue remains unclear, as short-term frequency analysis cannot reliably predict muscle fatigue 11 , 13 , 15 , 27 , 41 – 43 . In addition, sensor preparation, including sensor placement, adhesion of the sensors to the skin, and the location of the sensor relative to the active area of muscle contraction are crucial to obtain quality signals from the muscles of interest, and may vary between participants 12 .…”

Analysis of the Biceps Brachii Muscle by Varying the Arm Movement Level and Load Resistance Band

“…It was necessary to prepare the skin by cleaning the desired skin area using 70% isopropyl alcohol and shaving the hair, if necessary, in order to reduce the electrode-skin impedance [ 16 ]. The preferred placement of the EMG electrodes on the biceps brachii muscle, as suggested in previous works, is in the middle of the biceps brachii muscle, known as the belly muscle, as it shows a significantly higher amplitude [ 17 ]. All the protocols were designed to minimize the motion artefact, crosstalk, and internal noise during the EMG measurement.…”

“…In the denoising signal, several common MWT functions, such as Daubechies, Coiflet, and Symlet, are used. The selection of the best wavelet function and depth of decomposition is required to produce a perfect reconstruction and better signal analysis [ 17 ]. The best MWT function and decomposition level were determined by calculating the signal to noise ratio (SNR) and root mean square error (RMSE), as given below [ 19 ].…”