Journal of Electromyography and Kinesiology最新文献

Knee abduction and hip adduction during functional tasks may indicate increased joint injury risk and discriminate between pathological and healthy people. Muscles’ neuromuscular variables such as amplitude (EMG_AMP) and onset (EMG_ONSET) have been used to explain kinematics. The study aimed to evaluate the correlation between two EMG variables of seven trunk and lower limb muscles and 3D kinematics during two tasks. Eighteen physically-active women participated in the study. The following variables were obtained during single-leg squat and anterior step-down: (i) EMG_AMP and EMG_ONSET of fibularis longus (FL), tibialis anterior (TA), vastus medialis (VM), biceps femoris (BF), gluteus medius (GMED), ipsilateral (OB_IL) and contralateral (OB_CL) external obliques and (ii) knee abduction and hip adduction angular displacement (initial angle – angle at 60° of knee flexion). Spearman’s correlation coefficient was calculated between kinematic and EMG variables. Greater knee abduction was correlated with delayed TA_ONSET, GMED_ONSET and OB_IL_ONSET during step-down. Greater hip adduction was correlated with lower VM_AMP, BF_AMP and delayed VM_ONSET during step-down. Although task-specific, these results suggest that EMG_ONSET may influence knee abduction, while both EMG_ONSET and EMG_AMP may affect hip adduction. The identification of muscle activation patterns in relation to kinematics may help the development of injury prevention and rehabilitation programs.

Trunk extensor muscle fatigue typically manifests as a decline in spectral content of surface electromyography. However, previous research on the relationship of this decline with trunk extensor muscle endurance have shown inconsistent results. The decline of spectral content mainly reflects the decrease in average motor unit action potential conduction velocity (CV). We evaluated whether the rate of change in CV, as well as two approaches employing the change in spectral content, are related to trunk extensor muscle endurance. Fourteen healthy male participants without a low-back pain history performed a non-strictly controlled static forward trunk bending trial until exhaustion while standing. For 13 participants, physiologically plausible CV estimates were obtained from high-density surface electromyography bilaterally from T6 to L5. Laterally between L1 and L2, the linear rate of CV change was strongly correlated to endurance time (R² = 0.79), whereas analyses involving the linear rate of change in spectral measures showed a lower (R² = 0.38) or no correlation. For medial electrode locations, estimating CV and its relationship with endurance time was less successful, while the linear rate of change in spectral measures correlated moderately to endurance time (R² = 0.44; R² = 0.56). This study provides guidance on monitoring trunk extensor muscle fatigue development using electromyography.

The primary objective of this study was to examine the degree of inter-limb asymmetry in impact force magnitudes and rates during the first and second landings of a drop vertical jump in adolescent athletes with a history of anterior cruciate ligament (ACL) reconstruction. We also compared the degree of asymmetry exhibited by the athletes who had undergone ACL reconstruction to a group of uninjured athletes. This study included 14 athletes who had undergone ACL reconstruction and 28 uninjured athletes, matched for age, sex, and sport. All athletes completed a double-leg drop vertical jump task. Peak vertical ground reaction forces (vGRFs) and loading rates were examined for both limbs during the first and second landings of the drop vertical jump. For the athletes who had undergone ACL reconstruction, peak vGRFs were 11.9% greater for the uninvolved limb vs. the involved limb during the first landing; however, peak vGRFs were only 2.4% greater for the uninvolved limb (vs. the involved limb) during the second landing. The athletes who had undergone ACL reconstruction exhibited greater asymmetry in peak vGRFs for the first landing compared to the uninjured athletes; however, there was no difference between the groups (ACL reconstruction, uninjured) for the second landing.

The biceps femoris long head (BFLH) gains its properties from internal elements (fascicles and tendinous tissues) which behaviors remain poorly understood across BFLH regions and dynamic tasks. The aim of this study was to assess the in vivo behaviors of fascicles and tendinous tissue in the proximal and distal regions of BFLH during different dynamic knee and hip tasks. Twenty males performed the Nordic hamstring exercise (NHE) (n = 9) and Romanian deadlift (RDL) (n = 11). Activation of the BFLH was assessed using surface electromyography signals. Ultrasound images of BFLH and kinematic data were used to estimate the interaction between fascicle and tendinous tissue. The fascicles changed less in length (p < 0.05) and contributed less to BFLH length change (p < 0.05) in NHE with higher activation (p < 0.05) relative to RDL. The higher pennation angle changes of BFLH were found in distal region compared to proximal region in both tasks (p < 0.05), while the activation of distal region was higher than activation of proximal region in NHE (p < 0.05). The BFLH length change was primarily contributed by the tendinous tissue during dynamic resistance tasks, and was contributed less by fascicles which operated more isometrically in knee-dominant NHE with higher activation relative to hip-dominant RDL. Regional differences in pennation angle change and activation during dynamic tasks suggest potential regional differences in the mechanical function of BFLH, warranting further investigation.

This tutorial intends to provide insight, instructions and “best practices” for those who are novices—including clinicians, engineers and non-engineers—in extracting electromyogram (EMG) amplitude from the bipolar surface EMG (sEMG) signal of voluntary contractions. A brief discussion of sEMG amplitude extraction from high density sEMG (HDsEMG) arrays and feature extraction from electrically elicited contractions is also provided.

This tutorial attempts to present its main concepts in a straightforward manner that is accessible to novices in the field not possessing a wide range of technical background (if any) in this area. Surface EMG amplitude, also referred to as the sEMG envelope [often implemented as root mean square (RMS) sEMG or average rectified value (ARV) sEMG], quantifies the voltage variation of the sEMG signal and is grossly related to the overall neural excitation of the muscle and to peripheral parameters.

The tutorial briefly reviews the physiological origin of the voluntary sEMG signal and sEMG recording, including electrode configurations, sEMG signal transduction, electronic conditioning and conversion by an analog-to-digital converter. These topics have been covered in greater detail in prior tutorials in this series. In depth descriptions of state-of-the-art methods for computing sEMG amplitude are then provided, including guidance on signal pre-conditioning, absolute value vs. square-law detection, selection of appropriate sEMG amplitude smoothing filters and attenuation of measurement noise. The tutorial provides a detailed list of best practices for sEMG amplitude estimation.

Tracking scapular motion can be challenging. More research is needed to determine the best practices for scapular measurement in multi-planar tasks. The purpose of this study was to compare the repeatability of scapular kinematics during a functional task protocol calculated from different calibration procedures. It was hypothesized that select poses would improve repeatability in specific tasks. The torso, humerus, and scapula were tracked with optical motion capture in two sessions for ten pain-free participants. Scapular calibrations were completed in five poses: neutral, maximum elevation, 90° abduction, hand to contralateral shoulder, and hand to back. Each participant completed eight functional tasks (Comb Hair, Wash Axilla, Tie Apron, Overhead Reach, Side Reach, Forward Transfer, Floor Lift, Overhead Lift). Scapular angles were calculated with five different calibration procedures and extracted at 30° increments of humeral elevation in each task. Mean difference, limits of agreement, intraclass correlations, and minimal detectable change (MDC) were calculated for each task and elevation level. The inclusion of different calibration poses did not markedly improve outcomes over the maximum elevation double calibration for most tasks. Using this calibration procedure, median MDCs were 10.0° for upward rotation, 13.7° for internal rotation, and 9.8° for tilt.

There is currently equivocal evidence regarding sex-related differences in measures of muscle force and torque control. To that end, we investigated sex differences in knee extensor muscle torque control, using both magnitude- and complexity-based measures, across contraction intensities typical of activities of daily living. 50 participants (25 male, median age [and interquartile range] 23.0 [20.0–33.0]; 25 female, median age [and interquartile range] 21.0 [20.0–40.5]) performed a series of intermittent isometric knee extensor contractions at 10, 20 and 40% maximal voluntary contraction (MVC). Torque was measured in N·m and torque control was quantified according to the magnitude (standard deviation [SD], coefficient of variation [CV]) and complexity (approximate entropy [ApEn], detrended fluctuation analysis [DFA] α) of torque fluctuations. Males exhibited a significantly greater absolute magnitude (i.e., SD) of knee extensor torque fluctuations during contractions at 10% (P = 0.011), 20% (P = 0.002) and 40% MVC (P = 0.003), though no sex differences were evident when fluctuations were normalised to mean torque output (i.e., CV). Males exhibited significantly lower ApEn during contractions at 10% (P = 0.002) and 20% MVC (P = 0.024) and significantly greater DFA α during contractions at 10% (P = 0.003) and 20% MVC (P = 0.001). These data suggest sex differences in muscle torque control strategies and highlight the need to consider both the magnitude and complexity of torque fluctuations when examining sex differences in muscle force control.

Assessing a patient’s musculoskeletal function during over-ground walking is a primary objective in post-stroke rehabilitation, due to the importance of walking recovery for everyday life. However, the quantitative assessment of musculoskeletal function currently requires lab-constrained equipment, and labor-intensive analyses, which hampers assessment in standard clinical settings. The development of fully wearable systems for the online estimation of muscle–tendon forces and resulting joint torque would aid clinical assessment of motor recovery, it would enhance the detection of neuro-muscular anomalies and it would consequently enable highly personalized treatments.

Here, we present a wearable technology that combines (1) a soft garment for the human leg sensorized with 64 flexible and dry electromyography (EMG) electrodes, (2) a generalized and automated algorithm for the localization of leg muscle sites, and (3) an EMG-driven musculoskeletal modeling framework for the estimation of ankle dorsi-plantar flexion torques.

Our results showed that the automated clustering algorithm could detect muscle locations in both healthy and post-stroke individuals. The estimated muscle-specific EMG envelopes could be used to drive forward person-specific musculoskeletal models and estimate resulting joint torques accurately across all healthy and post-stroke individuals and across different walking speeds (R2  > 0.82 and RMSD  < 0.16).

The technology we proposed opens new avenues for automated muscle localization and quantitative musculoskeletal function assessment during gait in both healthy and neurologically impaired individuals.

Introduction

The EMG filling factor is an index to quantify the degree to which an EMG signal has been filled. Here, we tested the validity of such index to analyse the EMG filling process as contraction force was slowly increased.

Methods

Surface EMG signals were recorded from the quadriceps muscles of healthy subjects as force was gradually increased from 0 to 40% MVC. The sEMG filling process was analyzed by measuring the EMG filling factor (calculated from the non-central moments of the rectified sEMG).

Results

(1) As force was gradually increased, one or two prominent abrupt jumps in sEMG amplitude appeared between 0 and 10% of MVC force in all the vastus lateralis and medialis.

(2) The jumps in amplitude were originated when a few large-amplitude MUPs, clearly standing out from previous activity, appeared in the sEMG signal.

(3) Every time an abrupt jump in sEMG amplitude occurred, a new stage of sEMG filling was initiated.

(4) The sEMG was almost completely filled at 2–12% MVC.

(5) The filling factor decreased significantly upon the occurrence of an sEMG amplitude jump, and increased as additional MUPs were added to the sEMG signal.

(6) The filling factor curve was highly repeatable across repetitions.

Conclusions

It has been validated that the filling factor is a useful, reliable tool to analyse the sEMG filling process. As force was gradually increased in the vastus muscles, the sEMG filling process occurred in one or two stages due to the presence of abrupt jumps in sEMG amplitude.

Surface EMG (sEMG) has been used to compare loading conditions during exercise. Studies often explore mean/median frequencies. This potentially misses more nuanced electrophysiological differences between exercise tasks. Therefore, wavelet-based analysis was used to evaluate electrophysiological characteristics in the sEMG signal of the quadriceps under both higher- and lower-torque (70 % and 30 % of MVC, respectively) isometric knee extension performed to momentary failure. Ten recreationally active adult males with previous resistance training experience were recruited. Using a within-session, repeated-measures, randomised crossover design, participants performed isometric knee extension whilst sEMG was collected from the vastus medialis (VM), rectus femoris (RF) and vastus lateralis (VL). Mean signal frequency showed similar characteristics in each condition at momentary failure. However, individual wavelets revealed different frequency component changes between the conditions. All frequency components increased during the low-torque condition. But low-frequency components increased, and high-frequency components decreased, in intensity throughout the high-torque condition. This resulted in convergence of the low-torque and high-torque trial wavelet characteristics towards the end of the low-torque trial. Our results demonstrate a convergence of myoelectric signal properties between low- and high-torque efforts with fatigue via divergent signal adaptations. Further work should disentangle factors influencing frequency characteristics during exercise tasks.