MMG Signals Research Articles

Bulk movement included in multi-channel mechanomyography: Similarity between mechanomyography of resting muscle and that of contracting muscle

Although mechanomyography (MMG) reflects local vibrations from contracting muscle fibers, it also includes bulk movement: deformation in global soft tissue around measuring points. To distinguish between them, we compared the multi-channel MMG of resting muscle, which dominantly reflected the bulk movement caused by arterial pulsations, to that of the contracting muscle. The MMG signals were measured at five points around the upper arms of 10 male subjects during resting and during isometric ramp contraction from 5% to 85% of maximal voluntary contraction (MVC) of the biceps brachii muscle. The characteristics of bulk movement were defined as the amplitude distribution and phase relation among the five MMG signals. The bulk movement characteristics during the rest state were not necessarily the same among the subjects. However, below 30Hz, each subject’s characteristics remained the same from the rest state (0% MVC) to the contracting state (80% MVC), at which the bulk movement mainly originates from muscle contraction activity. Results show that the MMG of the low frequency domain (<30Hz) includes bulk movement depending on the mechanical deformation characteristics of each subject’s body, for a wide range of muscle contraction intensities.

Neural Contributions to Concentric vs. Eccentric Exercise–Induced Strength Loss

The purpose of this study was to examine the strength, electromyographic (EMG), and mechanomyographic (MMG) responses after workouts designed to elicit fatigue and muscle damage vs. only fatigue. Thirteen men (mean ± SD age = 23.7 ± 2.2 years) performed 6 sets of 10 maximal concentric isokinetic (CONexercise) or eccentric isokinetic (ECCexercise) muscle actions of the dominant forearm flexors on 2 separate days. Before (PRE) and after (POST) these workouts, peak torque (PT), surface EMG, and MMG signals were measured during maximal concentric isokinetic, eccentric isokinetic, and isometric muscle actions of the forearm flexors. The subjects also visited the laboratory for a control (CTL) visit with quiet resting between the PRE and POST measurements, rather than performing the CONexercise or ECCexercise. The results showed that there were significant 26 and 25% decreases in PT after the CONexercise and ECCexercise, respectively, and these decreases were statistically equivalent for the concentric, eccentric, and isometric muscle actions. There were also 19 and 23% reductions in normalized EMG amplitude after the CONexercise and ECCexercise, respectively, but no changes in EMG mean frequency (MNF), MMG amplitude, or MMG MNF. These findings demonstrated a neural component(s) to the strength decrement after CONexercise and ECCexercise. It is possible that after these 2 types of exercise, activation of free nerve endings that are sensitive to muscle damage and pH changes resulted in inhibition of alpha motor neurons, causing decreased muscle activation and torque. These findings suggest that training programs designed to minimize strength loss during competition should consider the fact that at least some of this loss is because of neural factors.

Effects of local elastic compression on muscle strength, electromyographic, and mechanomyographic responses in the lower extremity

The purpose of this study was to investigate the effect of elastic compression on muscle strength, electromyographic (EMG), and mechanomyographic (MMG) responses of quadriceps femoris during isometric and isokinetic contractions. Twelve participants performed 5 s isometric maximal voluntary contractions (MVC) and 25 consecutive and maximal isokinetic knee extensions at 60 and 300 °/s with no (control, CC), medium (MC), and high (HC) compression applied to the muscle. The EMG and MMG signals were collected simultaneously with muscle isometric and isokinetic strength data. The results showed that the elevated compression did not improve peak torque, peak power, average power, total work, and regression of torque in the isometric and isokinetic contractions. However, the root mean squared value of EMG in both HC and MC significantly decreased compared with CC at 60 and 300 °/s ( p < 0.01). Furthermore, the EMG mean power frequency in HC was significantly higher than that in CC at 60 °/s ( p < 0.05) whereas no significant compression effect was found in the MMG mean power frequency. These findings provide preliminary evidence suggesting that the increase in local compression pressure may effectively increase muscle efficiency and this might be beneficial in reducing muscle fatigue during concentric isokinetic muscle contractions.

Mechanomyographic responses are not influenced by the innervation zone for the vastus medialis

The purposes of this study were to: (1) compare the power output-related patterns of absolute and normalized mechanomyographic (MMG) amplitude and mean power frequency (MPF) signals across the innervation zone (IZ) during incremental cycle ergometry; and (2) examine the influence of the IZ on the mean MMG amplitude and MPF values. Ten men performed incremental cycle ergometry tests to exhaustion. Surface MMG signals were recorded simultaneously from accelerometer arrangements placed on the vastus medialis over the IZ and proximal and distal to the IZ. The results of polynomial regression indicate that the best-fit model for each site was consistent for each subject for MMG amplitude, but not for MMG MPF. Two-way analyses of variance (ANOVAs) revealed no site × power output interaction (P > 0.05) for absolute and normalized MMG amplitude and MPF values. Our findings demonstrate that the IZ does not influence the MMG signal during dynamic exercise.

The effects of skinfold thicknesses and innervation zone on the mechanomyographic signal during cycle ergometry

The purpose of this study was to examine the effects of skinfold (SF) thicknesses at four locations on the vastus lateralis (VL) muscle and the placement of accelerometers relative to the innervation zone (IZ) on the mechanomyographic (MMG) amplitude and mean power frequency (MPF) responses during incremental cycle ergometry. Twenty adults (age ± SD = 23.8 ± 3.0 years) participated in the investigation. The MMG signals were detected during incremental cycle ergometry using four accelerometers placed on the right VL. Prior to the cycle ergometer test, SF thicknesses were measured. Simple linear regression analyses and one-way repeated measures analyses of variance (ANOVAs) were performed. The present study found that only 10% of the regression analyses and mean comparisons were significant ( p < 0.05). Furthermore, the accelerometer placed at the most proximal site (Prox 2) had significantly greater MMG amplitude and MMG MPF than accelerometers placed at more distal sites (Prox 1, Over IZ, and Dist). There were no significant differences, however, in SF thickness between accelerometer placement sites. In addition, the IZ had no effect on MMG amplitude and little effect on MMG MPF values. The results of the present study indicated that the SF thickness values and IZ did not affect the MMG signal.

A New Mechanomyographic Amplitude-Based Fatigue Threshold Test for Cycling

PURPOSE: The purposes of this study were to: a) apply the Dmax method previously used to determine the gas exchange threshold (GET) to the amplitude content of the mechanomyographic (MMG) signal to estimate the Dmax-MMG threshold; and b) compare the mean VO2 values from the Dmax-MMG, GET, and respiratory compensation point (RCP) during incremental cycle ergometry. METHODS: Ten males (mean ± SD age = 23.3 ± 3.7 yrs) performed an incremental cycle ergometer test to exhaustion. MMG signals from the vastus lateralis and VO2 were recorded. The contraction period for each pedal thrust (descending phase of the pedaling cycle) was determined from the MMG bursts. The GET and RCP values were determined by non-invasive gas exchange measurements using the V-slope method and defined as the VO2 value corresponding to the intersection of two linear regression lines derived separately from the data points below and above the breakpoints for the VCO2 vs. VO2, and VE vs. VCO2 relationships, respectively. The MMG amplitude (rms expressed in m·s2) values were calculated by averaging the last three complete pedal thrusts of each power output. The Dmax-MMG (i.e., MMG vs.VO2) was determined as the point on the third order polynomial regression curve that yielded the maximal perpendicular distance to the straight line formed by the first and the last data points. A one-way repeated measures ANOVA and Tukey post-hoc comparisons were used for mean comparison among thresholds. Pearson product moment correlations were used to determine the relationships among the three fatigue thresholds. RESULTS: The results of the present study indicated that the mean VO2 for the RCP (3.22 ± 0.22 L·min-1) was significantly greater (p < 0.05) than the GET (2.43 ± 0.26 L·min-1) and Dmax-MMG (2.63 ± 0.48 L·min-1). There was no significant difference, however, between the mean VO2 for the GET and Dmax-MMG. Furthermore, there were no significant correlations for the Dmax-MMG vs. GET (r = -0.29), Dmax-MMG vs. RCP (r = -0.04), or GET vs. RCP (r = 0.49). CONCLUSIONS: The non-significant mean difference between the GET and Dmax-MMG suggested that these thresholds may reflect a similar dimension of metabolic and neuromuscular fatigue. It is possible that like the GET, the Dmax-MMG demarcates the moderate from heavy exercise intensity domains.

The Relationship Between Skinfold Thicknesses and Mechanomyography At Different Locations on the Vastus Lateralis During Incremental Cycle Ergometry

Previous studies have indicated that skinfold thickness (SKF) acts as a low-pass filter to the mechanomyographic (MMG) signal. PURPOSE: The purpose of this study was to examine the effects of SKF at four different locations on the vastus lateralis (VL) muscle on MMG amplitude and mean power frequency (MPF) responses during incremental cycle ergometry. METHODS: Twenty adults (age ± SD = 23.8 ± 3.0 years) volunteered to participate in the investigation. An orientation session was performed to determine the location of the innervation zone (IZ) for the VL using a linear electrode array. The MMG signals from the VL muscle were detected during an incremental cycle ergometry test using four accelerometers placed on the right VL proximal (Prox1), over (Over IZ), and distal (Dist IZ) to the IZ. A fourth accelerometer was placed at the site recommended by the SENIAM Project (Prox2) for the placement of EMG electrodes. Prior to the test, three SKF were taken at each accelerometer placement site. The mean of the three SKF at each site were used for the analysis. The subjects were divided (at the median SKF value) into two groups of smaller (n = 10) and larger (n = 10) SKF values. Simple linear regression analyses were performed for SKF vs. MMG amplitude and MPF at each location for power outputs of 50, 75, 100, 125, 150, and 175W. Independent t-tests were also performed between the smaller and larger SKF groups for SKF measurements, MMG amplitude, and MMG MPF values at each accelerometer placement site. An alpha of p ≤ 0.05 was considered statistically significant for all analyses. RESULTS: For the 48 regression analyses, there were only 2 significant relationships (r = −0.461 and −0.519) between the SKF values and MMG MPF and 3 significant relationships between the SKF values and MMG amplitude (r = −0.535, −0.456, and −0.443). The independent t-tests showed that there were significant mean differences between the smaller and larger SKF groups for SKF measurements for all accelerometer placements sites. Of the 48 mean comparisons, only 1 was significantly different between the smaller and larger SKF groups for MMG MPF and 4 for MMG amplitude. CONCLUSIONS: The present study showed that, in general, SKF values were not correlated with the MMG signal. Only 10% of the regression analyses for SKF versus MMG MPF and MMG amplitude were statistically significant. In addition, only 10% of the mean comparisons between smaller and larger SKF groups for MMG MPF and MMG amplitude were significantly different. Thus, the results of the present study indicated that the larger SKF values did not attenuated the MMG signal. Other factors, including changes in force production and muscle length, as well as factors related to the accelerometer location, such as amount of muscle mass, muscle architecture, and/or motor unit territorial distribution may have had a greater effect on the MMG signal than SKF. PRACTICAL APPLICATIONS: These findings indicated that, in generall, smaller and larger SKF values did not correlate with the amplitude and frequency parameters of the MMG signal. Thus, there may be other anatomical, physiological, or methodological factors that have a greater effect on the MMG signal. These findings provide information about a methodological consideration necessary for the interpretation of the MMG signal.

Man-Machine Interface System for Neuromuscular Training and Evaluation Based on EMG and MMG Signals

This paper presents the UVa-NTS (University of Valladolid Neuromuscular Training System), a multifunction and portable Neuromuscular Training System. The UVa-NTS is designed to analyze the voluntary control of severe neuromotor handicapped patients, their interactive response, and their adaptation to neuromuscular interface systems, such as neural prostheses or domotic applications. Thus, it is an excellent tool to evaluate the residual muscle capabilities in the handicapped. The UVa-NTS is composed of a custom signal conditioning front-end and a computer. The front-end electronics is described thoroughly as well as the overall features of the custom software implementation. The software system is composed of a set of graphical training tools and a processing core. The UVa-NTS works with two classes of neuromuscular signals: the classic myoelectric signals (MES) and, as a novelty, the myomechanic signals (MMS). In order to evaluate the performance of the processing core, a complete analysis has been done to classify its efficiency and to check that it fulfils with the real-time constraints. Tests were performed both with healthy and selected impaired subjects. The adaptation was achieved rapidly, applying a predefined protocol for the UVa-NTS set of training tools. Fine voluntary control was demonstrated to be reached with the myoelectric signals. And the UVa-NTS demonstrated to provide a satisfactory voluntary control when applying the myomechanic signals.

Feasibility of using an artificial neural network model to estimate the elbow flexion force from mechanomyography

The goal of this study was to demonstrate the feasibility of using artificial neural network (ANN) models to estimate the elbow flexion forces from mechanomyography (MMG) under isometric muscle contraction and compare the performance of the ANN models with the performance from multiple linear regression (MLR) models. Five participants (mean±SD age=25.4±2.96 yrs) performed ten predefined and ten randomly ordered elbow flexions from 0% to 80% maximal voluntary contractions (MVCs). The MMG signals were recorded from the biceps brachii (BR) and brachioradialis (BRD), both of which contribute to elbow flexion. Inputs into the model included the root-mean-square (RMS), a temporal characterization feature, which resulted in a slightly higher signal-to-noise ratio (SNR) than when using the mean absolute value (MAV), and the zero-crossing (ZC) as spectral characterization features. Additionally, how the RMS and the ZC as model inputs affected the estimation accuracy was investigated. A cross-subject validation test was performed to determine if the established model of one subject could be applied to another subject. It was observed that the ANN model provided a more accurate estimation based on the values of the normalized root mean square error (NRMSE=0.141±0.023) and the cross-correlation coefficient (CORR=0.883±0.030) than the estimations from the MLR model (NRMSE=0.164±0.030, CORR=0.846±0.033). The estimation results from the same-subject validation test were significantly better than those of the cross-subject validation test. Thus, using an ANN model on a subject-by-subject basis to quantify and track changes in the temporal and spectral responses of MMG signals to estimate the elbow flexion force is a reliable method.

Spectral properties of electromyographic and mechanomyographic signals during isometric ramp and step contractions in biceps brachii

The purposes of this study were: (1) to apply wavelet and principal component analysis to quantify the spectral properties of the surface EMG and MMG signals from biceps brachii during isometric ramp and step muscle contractions when the motor units are recruited in an orderly manner, and (2) to compare the recruitment patterns of motor unit during isometric ramp and step muscle contractions. Twenty healthy participants (age = 34 ± 10.7 years) performed step and ramped isometric contractions. Surface EMG and MMG were recorded from biceps brachii. The EMGs and MMGs were decomposed into their intensities in time–frequency space using a wavelet technique. The EMG and MMG spectra were then compared using principal component analysis (PCA) and ANCOVA. Wavelet combined PCA offers a quantitative measure of the contribution of high and low frequency content within the EMG and MMG. The ANCOVA indicated that there was no significant difference in EMG total intensity, EMG MPF, first and second principal component loading scores (PCI and PCII) between ramp and step contractions, whereas the MMG MPF and MMG PCI loading scores were significantly higher during ramp contractions than during step contractions. These findings suggested that EMG and MMG may offer complimentary information regarding the interactions between motor unit recruitment and firing rate that control muscle force production. In addition, our results support the hypothesis that different motor unit recruitment strategy was used by the muscle when contracting under different conditions.

Passive stretching effects on electromechanical delay and time course of recovery in human skeletal muscle: new insights from an electromyographic and mechanomyographic combined approach

Acute passive stretching has been shown to alter muscle-tendon unit (MTU) stiffness and to reduce peak tetanic force (pF). MTU mechanical properties and electro-mechanical delay (EMD) are closely related. Thus, EMD changes would be expected after stretching. The aim of the study was to assess the stretching-induced changes in both contractile and viscoelastic contributors to EMD. The time course of these changes will be also evaluated. Tetanic stimulations were delivered on the medial gastrocnemius muscle of 16 active males, before and after (every 15 min, for 2 h) passive stretching administration. During contractions, electromyographic (EMG), mechanomyographic (MMG) and force signals were recorded. The delays between EMG and force (Δt EMG-F, which corresponds to EMD), EMG and MMG (Δt EMG-MMG) and MMG and force (Δt MMG-F) signals were calculated, together with pF and EMG conduction velocity (CV). After stretching (i) pF decreased by 31% (P < 0.05) and remained depressed for the entire recovery period, while EMG CV did not change; (ii) Δt EMG-F, Δt EMG-MMG and Δt MMG-F increased significantly from 45.4 ± 3.0 ms, 2.2 ± 0.3 ms and 42.4 ± 3.1 ms to 52.7 ± 3.4 ms, 2.4 ± 0.3 ms and 50.3 ± 3.5 ms, respectively; (iii) Δt EMG-F and Δt MMG-F remained lengthened for the entire recovery period, while Δt EMG-MMG recovered to its pre-stretching condition within 15 min. These findings suggest that after stretching, the reduction in pF was accompanied by an elongation of the overall EMD. However, stretching had effects of short duration at the contractile level, but more persisting effects on MTU viscoelastic characteristics.

Relationships among peak power output, peak bar velocity, and mechanomyographic amplitude during the free-weight bench press exercise

The purpose of this study was to examine the relationships among mechanomyographic (MMG) amplitude, power output, and bar velocity during the free-weight bench press exercise. Twenty-one resistance-trained men [one-repetition maximum (1-RM) bench press = 125.4 ± 18.4 kg] performed bench press muscle actions as explosively as possible from 10% to 90% of the 1-RM while peak power output and peak bar velocity were assessed with a TENDO Weightlifting Analyzer. During each muscle action, surface MMG signals were detected from the right and left pectoralis major and triceps brachii, and the concentric portion of the range of motion was selected for analysis. Results indicated that power output increased from 10% to 50% 1-RM, followed by decreases from 50% to 90% 1-RM, but MMG amplitude for each of the muscles increased from 10 to 80%1-RM. The results of this study indicate that during the free-weight bench press exercise, MMG amplitude was not related to power output, but was inversely related to bar velocity and directly related to the external load being lifted. In future research, coaches and sport scientists may be able to estimate force/torque production from individual muscles during multi-joint, dynamic constant external resistance muscle actions.

Linearity and reliability of the mechanomyographic amplitude versus dynamic constant external resistance relationships for the biceps brachii

The purpose of this study was to examine the linearity and reliability of the mechanomyographic (MMG) amplitude versus concentric and eccentric dynamic constant external resistance (DCER) relationships for the biceps brachii. Nineteen healthy men (mean ± SD age = 22.9 ± 2.5 years) performed submaximal to maximal unilateral DCER muscle actions of the dominant forearm flexors on two occasions separated by at least 48 h. During each muscle action, the surface MMG signal was detected from the biceps brachii with an accelerometer. The coefficients of determination ranged from 0.01 to 0.90 for the concentric muscle actions and 0.14 to 0.88 for the eccentric muscle actions, thus demonstrating a wide range of linearity. The intra-class correlation coefficients and standard errors of measurement for the linear MMG amplitude versus DCER slope coefficients were 0.361 (48.0%) for the concentric muscle actions and 0.512 (35.5%) for the eccentric muscle actions, indicating poor reliability. These findings demonstrated that the MMG amplitude versus concentric and eccentric DCER relationships were not consistently linear or reliable.

An Analysis of Agreement between MMG vs. EMG Systems for Identification of Nerve Location During Spinal Procedures

Neural injury is a major risk of surgical procedures. Traditional surgical exposures emphasize landmark recognition in order to avoid neural injury. As newer MIS techniques develop, neural protection has become an ever-increasing challenge. The utilization of intraoperative neurophysiologic monitoring is becoming a common tool to aid surgeons with neural identification and avoidance. Standard techniques for nerve localization and avoidance employ electromyography (EMG). EMG functions by measuring the electrical activity generated by muscle cells during contraction. This electrical activity is measured and recorded by either surface electrodes or percutaneous needle electrodes. EMG signals are prone to interference, and the analysis of EMG signals is a complex process that is poorly understood by most surgeons, usually requiring specialty training for interpretation. An alternative to EMG, Mechanomyography (MMG), functions by detecting and measuring the motion of contracting muscle using accelerometry. Interpretation of an MMG signal does not require a specialty trained technician or neurologist, it is simply “STOP - the muscle is contracting… a nerve must be nearby” or “GO - no muscle activity detected”.

Comparing electromyographic and mechanomyographic frequency-based fatigue thresholds to critical torque during isometric forearm flexion

Theoretically, the critical torque (CT), electromyographic mean power frequency fatigue threshold (EMG MPF FT), and mechanomyographic mean power frequency fatigue threshold (MMG MPF FT) describe the maximal non-fatiguing isometric torque level. Purpose The purposes of this study were threefold: (1) to determine if the mathematical model for estimating the EMG MPF FT during isometric muscle actions of the leg extensors was applicable to isometric muscle actions of the forearm flexors; (2) to determine if the mathematical model for estimating the EMG MPF FT from the frequency of the EMG signal was applicable to the frequency domain of the MMG signal to estimate a new fatigue threshold called the mechanomyographic mean power frequency fatigue threshold (MMG MPF FT); and (3) to compare the mean torque levels derived from the CT, EMG MPF FT, and MMG MPF FT tests during isometric forearm flexion muscle actions. Methods Ten adults (4 men and 6 women, mean ± SD; age = 22.0 ± 2.1 years) performed three or four continuous, fatiguing, isometric muscle actions of the forearm flexors at 30, 45, 60, and 75% of maximum voluntary isometric contraction (MVIC) to determine the time to exhaustion ( T lim) values. The slope coefficient of the linear relationship between total isometric “work” ( W lim in N m s = torque × T lim) and T lim was defined as the CT. Surface EMG and MMG signals were recorded from the biceps brachii muscle during each fatiguing isometric muscle action. The EMG and MMG MPF FT were defined as the y-intercepts of the isometric torque versus slope coefficient (EMG and MMG MPF versus time) plots. Results There were no significant differences between fatigue thresholds (CT = 26.3 ± 0.8, EMG MPF FT = 31.4 ± 4.2, and MMG MPF FT = 29.5 ± 7.0%MVIC), and the mean torque values (N m) from the three fatigue thresholds were significantly intercorrelated at r = 0.94–0.96. Conclusion The EMG MPF FT test may provide a non-invasive method to examine the effects of interventions on the conduction velocity and shape of the action potential waveform. In addition, the effects of interventions on the global motor unit firing rate of the unfused, activated motor units may be examined by the non-invasive methods of the MMG MPF FT test.

A Mechanomyographic Fatigue Threshold Test for Cycling

The purposes of this study were twofold: 1) to derive the mechanomyographic mean power frequency fatigue threshold (MMG MPFFT) for submaximal cycle ergometry; and 2) to compare the power outputs associated to the MMG MPFFT to other neuromuscular and gas exchange fatigue thresholds. 9 adults (5 men and 4 women; mean+/-SD age=23.7+/-3.7 years; body weight=66.3+/-8.2 kg) performed an incremental cycle ergometry test to exhaustion while expired gas samples, electromyographic (EMG), and MMG signals were measured from the vastus lateralis muscle. The non-significant correlations (r=0.17 to 0.66; p>0.05) among the physical working capacity at the fatigue threshold (PWCFT), MMG MPFFT, and gas exchange threshold (GET) suggested that different physiological mechanisms may underlie these 3 fatigue thresholds. A significant correlation (r=0.83) for the MPFFT vs. respiratory compensation point (RCP) suggested that these fatigue thresholds may be mediated by a common physiological mechanism. In addition, the significantly lower mean values found for the PWCFT (mean+/-SD=163+/-43 W), MMG MPFFT (132+/-33 W), and GET (144+/-28 W) than MPFFT (196+/-53 W) and RCP (202+/-41 W) suggested that these gas exchange and neuromuscular fatigue thresholds may demarcate different exercise intensity domains.

Estimation of elbow flexion force during isometric muscle contraction from mechanomyography and electromyography

Mechanomyography (MMG) is the muscle surface oscillations that are generated by the dimensional change of the contracting muscle fibers. Because MMG reflects the number of recruited motor units and their firing rates, just as electromyography (EMG) is influenced by these two factors, it can be used to estimate the force exerted by skeletal muscles. The aim of this study was to demonstrate the feasibility of MMG for estimating the elbow flexion force at the wrist under an isometric contraction by using an artificial neural network in comparison with EMG. We performed experiments with five subjects, and the force at the wrist and the MMG from the contributing muscles were recorded. It was found that MMG could be utilized to accurately estimate the isometric elbow flexion force based on the values of the normalized root mean square error (NRMSE = 0.131 ± 0.018) and the cross-correlation coefficient (CORR = 0.892 ± 0.033). Although MMG can be influenced by the physical milieu/morphology of the muscle and EMG performed better than MMG, these experimental results suggest that MMG has the potential to estimate muscle forces. These experimental results also demonstrated that MMG in combination with EMG resulted in better performance estimation in comparison with EMG or MMG alone, indicating that a combination of MMG and EMG signals could be used to provide complimentary information on muscle contraction.

Comparison of a New Mechanomyographic Frequency-Based Fatigue Threshold Test and Critical Torque

Theoretically, the critical torque (CT) test estimates the maximal, non-fatiguing isometric torque level. In addition, it has been suggested that changes in the mechanomyographic (MMG) mean power frequency (MPF) provide qualitative information regarding the global firing rate of the activated motor units. PURPOSE: The purposes of this study were twofold: 1) to determine if the mathematical model for estimating the electromyographic (EMG) fatigue threshold (EMGFT) from the amplitude of the EMG signal was applicable to the frequency domain of the MMG signal to estimate a new fatigue threshold called the MMG MPFFT; and 2) to compare the torque level derived from the CT test to that of the MMG MPFFT test for the vastus lateralis (VL) muscle during isometric muscle actions of the leg extensors. METHODS: Nine adults (4 men and 5 women; mean ± SD age = 21.6 ± 1.2 yr) performed three or four continuous, fatiguing, isometric muscle actions of the leg extensors at 30, 45, 60, and 75 % of maximum voluntary isometric contraction (MVIC) to determine the time to exhaustion (Tlim) values. The slope coefficient of the linear relationship between total isometric "work" (Wlim in N·m·s = Torque × Tlim) and Tlim was defined as the CT. Surface MMG signals were recorded from the VL during each fatiguing isometric muscle action. The MMG MPFFT was defined as the y-intercept of the isometric torque versus slope coefficient (MMG MPF versus time) plot. RESULTS: There were no significant differences (p 0.05) between absolute (25.2 ± 11.4 and 34.8 ± 23.4 N·m, respectively) and %MVIC (19.7 ± 5.8 and 23.7 ± 7.8 %, respectively) values for CT and MMG MPFFT. CONCLUSION: The non-significant mean difference between CT and MMG MPFFT suggests that continuous isometric leg extension muscle actions at the MMG MPFFT could be maintained for an extended period of time without fatigue. Furthermore, the MMG MPFFT test may provide a non-invasive method to examine the effects of various interventions on the global motor unit firing rate during isometric muscle actions.

An examination of cross-talk among surface mechanomyographic signals from the superficial quadriceps femoris muscles during isometric muscle actions

The purpose of this study was to examine cross-talk among the mechanomyographic (MMG) signals from the superficial quadriceps femoris muscles during submaximal to maximal isometric muscle actions of the leg extensors. Eleven healthy men (age = 20.1 ± 1.1 yr, mean ± SD) volunteered to randomly perform isometric muscle actions in 10% increments from 10% to 90% of the maximum voluntary contraction (MVC). During each muscle action, MMG signals were detected from the vastus lateralis, rectus femoris, and vastus medialis with three separate accelerometers. Cross-correlation was used to quantify cross-talk among the vastus lateralis, rectus femoris, and vastus medialis during each muscle action. The results showed cross-correlation coefficients that ranged from R x,y = .124–.714, but generally speaking, the coefficients were between .1 and .3. In addition, there were no consistent differences among the cross-talk levels for the three muscles, and the cross-correlation coefficients generally did not increase with isometric torque. Thus, MMG can be used to examine muscle function from each of the superficial quadriceps femoris muscles during isometric muscle actions.

Linearity and Reliability of the Mechanomyographic Amplitude Versus Concentric Dynamic Constant External Resistance Relationships for the Bench Press Exercise

The purpose of the present study was to examine the linearity and reliability of the mechanomyographic (MMG) amplitude versus concentric dynamic constant external resistance (DCER) relationships for the bench press exercise. Twenty-one resistance-trained men (mean +/- SD age = 23.5 +/- 2.7 yr; 1 repetition maximum [1RM] bench press = 125.4 +/- 18.4 kg) volunteered to perform submaximal bench press muscle actions as explosively as possible from 10% to 90% of the 1RM on 2 separate occasions. During each muscle action, surface MMG signals were detected from both the right and left pectoralis major and triceps brachii, and the concentric portion of the range of motion was selected for analysis. The coefficients of determination for the MMG amplitude versus concentric DCER relationships ranged from r2 = 0.010 to 0.980 for the right pectoralis major, r2 = 0.010 to 0.943 for the left pectoralis major, r2 = 0.010 to 0.920 for the right triceps brachii, and r2 = 0.020 to 0.915 for the left triceps brachii, thus indicating a wide range of linearity between subjects. The intraclass correlation coefficients (ICC) and corresponding standard error of measurements (SEM) for the linear slope coefficients for these relationships were 0.592 (39.3% of the mean value), 0.537 (41.9% of the mean value), 0.625 (42.0% of the mean value), and 0.460 (60.2% of the mean value) for the right pectoralis major, the left pectoralis major, the right triceps brachii, and the left triceps brachii, respectively. These data demonstrated that these relationships were neither linear nor reliable enough to be used for assessing issues such as the neural versus hypertrophic contributions to training-induced strength gains and the mechanisms underlying cross-education.