Mechanomyographic Signals Research Articles

Respiratory rate detection by empirical mode decomposition method applied to diaphragm mechanomyographic signals.

Non-invasive evaluation of respiratory activity is an area of increasing research interest, resulting in the appearance of new monitoring techniques, ones of these being based on the analysis of the diaphragm mechanomyographic (MMGdi) signal. The MMGdi signal can be decomposed into two parts: (1) a high frequency activity corresponding to lateral vibration of respiratory muscles, and (2) a low frequency activity related to excursion of the thoracic cage. The purpose of this study was to apply the empirical mode decomposition (EMD) method to obtain the low frequency of MMGdi signal and selecting the intrinsic mode functions related to the respiratory movement. With this intention, MMGdi signals were acquired from a healthy subject, during an incremental load respiratory test, by means of two capacitive accelerometers located at left and right sides of rib cage. Subsequently, both signals were combined to obtain a new signal which contains the contribution of both sides of thoracic cage. Respiratory rate (RR) measured from the mechanical activity (RR(MMG)) was compared with that measured from inspiratory pressure signal (RR(P)). Results showed a Pearson's correlation coefficient (r = 0.87) and a good agreement (mean bias = -0.21 with lower and upper limits of -2.33 and 1.89 breaths per minute, respectively) between RR(MMG) and RR(P) measurements. In conclusion, this study suggests that RR can be estimated using EMD for extracting respiratory movement from low mechanical activity, during an inspiratory test protocol.

The effects of gender and very short-term resistance training on peak torque, average power and neuromuscular responses of the forearm flexors

OBJECTIVE: This study examined the effects of sex on peak torque (PT), average power (AP), and the time (AMP) and frequency (MPF) domain parameters of the electromyographic (EMG) and mechanomyographic (MMG) signals following very short-term resistance training (VST) of the forearm flexors. Based on the results of previous studies it was hypothesized that three training sessions would increase PT, EMG MPF, and MMG MPF of the forearm flexors at 60 and 180 ◦ /s in men only, without changes to AP, EMG AMP, or MMG AMP for either men or women. METHODS: Nine men and nine women completed two pretests, three training sessions, and a posttest that included concentric isokinetic muscle actions of the forearm flexors at 60 and 180 ◦ /s. The AMP and MPF of the EMG and MMG signals were recorded from the biceps brachii. RESULTS: The results indicate increases in PT, AP, and MMG AMP at 60 and 180 ◦ /s for the men only, but no changes in EMG AMP, EMG MPF, or MMG MPF for either the men or women. CONCLUSION: There were gender differences in the PT and AP responses to VST of the forearm flexors that were not associated with increased agonist muscle activation. These findings have implications for the development of gender-specific VST programs for the forearm flexors in clinical settings.

Novel pseudo-wavelet function for MMG signal extraction during dynamic fatiguing contractions.

The purpose of this study was to develop an algorithm to classify muscle fatigue content in sports related scenarios. Mechanomyography (MMG) signals of the biceps muscle were recorded from thirteen subjects performing dynamic contractions until fatigue. For training and testing purposes, the signals were labeled in two classes (Non-Fatigue and Fatigue). A genetic algorithm was used to evolve a pseudo-wavelet function for optimizing the detection of muscle fatigue. Tuning of the generalized evolved pseudo-wavelet function was based on the decomposition of 70% of the conducted MMG trials. After completing 25 independent pseudo-wavelet evolution runs, the best run was selected and then tested on the remaining 30% of the data to measure the classification performance. Results show that the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.70 percentage points to 16.61 percentage points when compared to other standard wavelet functions, giving an average correct classification of 80.63%, with statistical significance (p < 0.05).

Cross-Talk in Mechanomyographic Signals from the Forearm Muscles during Sub-Maximal to Maximal Isometric Grip Force

PurposeThis study aimed: i) to examine the relationship between the magnitude of cross-talk in mechanomyographic (MMG) signals generated by the extensor digitorum (ED), extensor carpi ulnaris (ECU), and flexor carpi ulnaris (FCU) muscles with the sub-maximal to maximal isometric grip force, and with the anthropometric parameters of the forearm, and ii) to quantify the distribution of the cross-talk in the MMG signal to determine if it appears due to the signal component of intramuscular pressure waves produced by the muscle fibers geometrical changes or due to the limb tremor.MethodsTwenty, right-handed healthy men (mean ± SD: age = 26.7±3.83 y; height = 174.47±6.3 cm; mass = 72.79±14.36 kg) performed isometric muscle actions in 20% increment from 20% to 100% of the maximum voluntary isometric contraction (MVIC). During each muscle action, MMG signals generated by each muscle were detected using three separate accelerometers. The peak cross-correlations were used to quantify the cross-talk between two muscles.ResultsThe magnitude of cross-talk in the MMG signals among the muscle groups ranged from, R2x, y = 2.45–62.28%. Linear regression analysis showed that the magnitude of cross-talk increased linearly (r2 = 0.857–0.90) with the levels of grip force for all the muscle groups. The amount of cross-talk showed weak positive and negative correlations (r2 = 0.016–0.216) with the circumference and length of the forearm respectively, between the muscles at 100% MVIC. The cross-talk values significantly differed among the MMG signals due to: limb tremor (MMGTF), slow firing motor unit fibers (MMGSF) and fast firing motor unit fibers (MMGFF) between the muscles at 100% MVIC (p<0.05, η 2 = 0.47–0.80).SignificanceThe results of this study may be used to improve our understanding of the mechanics of the forearm muscles during different levels of the grip force.

Effects of Diverting Activity on Strength, Electromyographic, and Mechanomyographic Signals

The purpose of this study was to investigate the effects of different recovery interventions on peak torque, electromyographic (EMG), and mechanomyographic (MMG) measures. Ten (23.40 ± 1.00 years; 178.40 ± 5.03 cm; 84.80 ± 15.85 kg) recreationally trained college men performed 4 experimental visits consisting of 2 bouts of 50 maximal isokinetic leg extensions at 180°·s(-1). Between each bout of maximal exercise, 2 minutes of recovery involving one of the 4 interventions (passive, active, passive diverting, and active diverting) was completed. Electromyographic and MMG measures were collected during the preintervention and postintervention maximal isokinetic strength tests. Peak torque declined to a significantly greater degree during the postintervention test in the passive condition than the other interventions. Electromyographic amplitude decreased on the postintervention test but did not differ between conditions. There was a significant 2-way interaction for EMG mean power frequency (MPF) between time and repetitions. There was a significant decrease in EMG MPF from the first 3 to the last 3 repetitions for both the preintervention and postintervention tests. The decrease in EMG MPF from the initial to final repetitions was greater for the preintervention test than the postintervention test. Mechanomyographic amplitude significantly decreased from the initial to final repetitions regardless of time or condition. These findings suggest that active, passive diverting, and active diverting strategies provide the same extent of recovery between maximal, fatiguing isokinetic leg extension tasks, and that these strategies can be used by coaches and exercise professionals to select the most effective interset recovery strategy.

Evidence towards Improved Estimation of Respiratory Muscle Effort from Diaphragm Mechanomyographic Signals with Cardiac Vibration Interference Using Sample Entropy with Fixed Tolerance Values

The analysis of amplitude parameters of the diaphragm mechanomyographic (MMGdi) signal is a non-invasive technique to assess respiratory muscle effort and to detect and quantify the severity of respiratory muscle weakness. The amplitude of the MMGdi signal is usually evaluated using the average rectified value or the root mean square of the signal. However, these estimations are greatly affected by the presence of cardiac vibration or mechanocardiographic (MCG) noise. In this study, we present a method for improving the estimation of the respiratory muscle effort from MMGdi signals that is robust to the presence of MCG. This method is based on the calculation of the sample entropy using fixed tolerance values (fSampEn), that is, with tolerance values that are not normalized by the local standard deviation of the window analyzed. The behavior of the fSampEn parameter was tested in synthesized mechanomyographic signals, with different ratios between the amplitude of the MCG and clean mechanomyographic components. As an example of application of this technique, the use of fSampEn was explored also in recorded MMGdi signals, with different inspiratory loads. The results with both synthetic and recorded signals indicate that the entropy parameter is less affected by the MCG noise, especially at low signal-to-noise ratios. Therefore, we believe that the proposed fSampEn parameter could improve estimates of respiratory muscle effort from MMGdi signals with the presence of MCG interference.

A hybrid symplectic principal component analysis and central tendency measure method for detection of determinism in noisy time series with application to mechanomyography

We present a hybrid symplectic geometry and central tendency measure (CTM) method for detection of determinism in noisy time series. CTM is effective for detecting determinism in short time series and has been applied in many areas of nonlinear analysis. However, its performance significantly degrades in the presence of strong noise. In order to circumvent this difficulty, we propose to use symplectic principal component analysis (SPCA), a new chaotic signal de-noising method, as the first step to recover the system dynamics. CTM is then applied to determine whether the time series arises from a stochastic process or has a deterministic component. Results from numerical experiments, ranging from six benchmark deterministic models to 1/f noise, suggest that the hybrid method can significantly improve detection of determinism in noisy time series by about 20 dB when the data are contaminated by Gaussian noise. Furthermore, we apply our algorithm to study the mechanomyographic (MMG) signals arising from contraction of human skeletal muscle. Results obtained from the hybrid symplectic principal component analysis and central tendency measure demonstrate that the skeletal muscle motor unit dynamics can indeed be deterministic, in agreement with previous studies. However, the conventional CTM method was not able to definitely detect the underlying deterministic dynamics. This result on MMG signal analysis is helpful in understanding neuromuscular control mechanisms and developing MMG-based engineering control applications.

MEASUREMENT AND ESTIMATION OF MUSCLE CONTRACTION STRENGTH USING MECHANOMYOGRAPHY BASED ON ARTIFICIAL NEURAL NETWORK ALGORITHM

Muscle contraction strength estimation using mechanomyographic (MMG) signal is typically calculated by the root mean square (RMS) amplitude. Raw MMG signal is processed by rectification, low-pass filtering, and mapping. In this work, beside RMS amplitude, another significant parameter of MMG signal, i.e. frequency variance (VAR), is introduced and used for constructing an algorithm for estimating the muscle contraction strength. Seven participants produced isometric contractions about the elbow while MMG signal and generated torque (resultant of muscle contraction strength) of biceps brachii were recorded. We found that MMG RMS increased monotonously and VAR decreased under incremental voluntary contractions. Based on these results, a two-layer neural network was utilized for the model of estimating the muscle contraction strength from MMG RMS and VAR. Experimental evaluation was performed under constant posture and sinusoidal contractions at 0.5 Hz, 0.25 Hz, 0.125 Hz, and random frequency. The results of the proposed algorithm and MMG RMS linear mapping were also compared. The proposed algorithm has better accuracy than linear mapping for all contraction frequencies. The mean absolute error decreased 6% for the 0.5Hz contraction, 43% for 0.25 Hz contraction, 52% for 0.125 Hz contraction, and 30% for random frequency contraction.

Index for estimation of muscle force from mechanomyography based on the Lempel–Ziv algorithm

The study of the amplitude of respiratory muscle mechanomyographic (MMG) signals could be useful in clinical practice as an alternative non-invasive technique to assess respiratory muscle strength. The MMG signal is stochastic in nature, and its amplitude is usually estimated by means of the average rectified value (ARV) or the root mean square (RMS) of the signal. Both parameters can be used to estimate MMG activity, as they correlate well with muscle force. These estimations are, however, greatly affected by the presence of structured impulsive noise that overlaps in frequency with the MMG signal. In this paper, we present a method for assessing muscle activity based on the Lempel–Ziv algorithm: the Multistate Lempel–Ziv (MLZ) index. The behaviour of the MLZ index was tested with synthesised signals, with various amplitude distributions and degrees of complexity, and with recorded diaphragm MMG signals. We found that this index, like the ARV and RMS parameters, is positively correlated with changes in amplitude of the diaphragm MMG components, but is less affected by components that have non-random behaviour (like structured impulsive noise). Therefore, the MLZ index could provide more information to assess the MMG–force relationship.

Effect of recruitment of new MUs on mechanomyographic signal during prolonged isometric constant contractions at low level

Effect of recruitment of new MUs on mechanomyographic signal during prolonged isometric constant contractions at low level

Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees

BackgroundElectromyography (EMG) pattern-recognition based control strategies for multifunctional myoelectric prosthesis systems have been studied commonly in a controlled laboratory setting. Before these myoelectric prosthesis systems are clinically viable, it will be necessary to assess the effect of some disparities between the ideal laboratory setting and practical use on the control performance. One important obstacle is the impact of arm position variation that causes the changes of EMG pattern when performing identical motions in different arm positions. This study aimed to investigate the impacts of arm position variation on EMG pattern-recognition based motion classification in upper-limb amputees and the solutions for reducing these impacts.MethodsWith five unilateral transradial (TR) amputees, the EMG signals and tri-axial accelerometer mechanomyography (ACC-MMG) signals were simultaneously collected from both amputated and intact arms when performing six classes of arm and hand movements in each of five arm positions that were considered in the study. The effect of the arm position changes was estimated in terms of motion classification error and compared between amputated and intact arms. Then the performance of three proposed methods in attenuating the impact of arm positions was evaluated.ResultsWith EMG signals, the average intra-position and inter-position classification errors across all five arm positions and five subjects were around 7.3% and 29.9% from amputated arms, respectively, about 1.0% and 10% low in comparison with those from intact arms. While ACC-MMG signals could yield a similar intra-position classification error (9.9%) as EMG, they had much higher inter-position classification error with an average value of 81.1% over the arm positions and the subjects. When the EMG data from all five arm positions were involved in the training set, the average classification error reached a value of around 10.8% for amputated arms. Using a two-stage cascade classifier, the average classification error was around 9.0% over all five arm positions. Reducing ACC-MMG channels from 8 to 2 only increased the average position classification error across all five arm positions from 0.7% to 1.0% in amputated arms.ConclusionsThe performance of EMG pattern-recognition based method in classifying movements strongly depends on arm positions. This dependency is a little stronger in intact arm than in amputated arm, which suggests that the investigations associated with practical use of a myoelectric prosthesis should use the limb amputees as subjects instead of using able-body subjects. The two-stage cascade classifier mode with ACC-MMG for limb position identification and EMG for limb motion classification may be a promising way to reduce the effect of limb position variation on classification performance.

Effects of Warm-up on Peak Torque, Rate of Torque Development, and Electromyographic and Mechanomyographic Signals

The purpose of this study was to determine if an active warm-up affects peak torque (PT), rate of torque development (RTD), and the electromyographic (EMG) and mechanomyographic (MMG) signals. Twenty-one men (mean age ± SD: 24.0 ± 2.7 years) visited the exercise physiology laboratory on 2 occasions. During the first visit, they either performed an active warm-up (10 minutes of stationary cycling at 70% of predicted maximum heart rate) or sat quietly (no warm-up). Participants were then tested for isometric and isokinetic (60°, 180°, and 300°·s) PT, and RTD (measured as S-gradient) on an isokinetic dynamometer. Electromyographic and MMG sensors were placed over the vastus lateralis muscle to monitor the electrical and mechanical aspects of muscle contractions, respectively. The testing protocol used for the first visit was repeated for the second visit, but the preexercise treatment (warm-up, no warm-up) not given during the first visit was administered. The results indicated that an active warm-up did not affect PT, RTD, or measures of muscle activation as reflected by EMG amplitude, EMG frequency, or MMG frequency (p > 0.05). However, MMG amplitude at 180°·s was significantly greater in the warm-up condition compared with the no warm-up condition. The isolated increase in MMG amplitude suggested that warm-up may have affected the mechanical properties of muscle by reducing muscular stiffness or decreasing intramuscular fluid pressure, but that it was not sufficient to influence performance.

Comparison of an accelerometer and piezoelectric contact sensor for examining the mechanomyographic signal from the vastus medialis during isometric muscle actions

Background and objective: The purpose of this study was to compare an accelerometer (ACC) and a piezoelectric contact sensor (PEC) for the measurement of mechanomyographic (MMG) signals during submaximal to maximal isometric muscle actions of the vastus medialis. Materials and methods: Following the determination of maximal isometric strength (MVC), eight subjects (20.4 ± 1.3 years) performed isometricmuscle actions at 10 to 90% isometric MVC in random order. AnACC and PEC were used to simultaneously record the MMG signal. Results: For the composite data, there were linear increases in normalized MMG amplitude for both the ACC (r 2 = 0.930) and PEC (r 2 = 0.875), with no difference in slope coefficients ( p> 0.05). Similarly, there were linear increases in MMG mean power frequency (MPF) for the ACC (r 2 = 0.957) and PEC (r 2 = 0.982), with no difference in slope coefficients ( p> 0.05). Analysis of individual subject data revealed there were no significant differences in slope coefficients between sensors for MMG amplitude or MPF for any subject with the exception of a significant difference ( p 0.05) mean differences between the ACC and PEC for normalized MMG amplitude or MPF. Conclusion: These results indicated that the ACC and PEC provide comparable information regarding MMG amplitude and MPF vs. isometric torque relationships of the vastus medialis.

Spectral properties of electromyographic and mechanomyographic signals during dynamic concentric and eccentric contractions of the human biceps brachii muscle

The purpose of this study was to describe and examine the variations in recruitment patterns of motor units (MUs) in biceps brachii (BB) through a range of joint motion during dynamic eccentric and concentric contractions. Twelve healthy participants (6 females, 6 males, age = 30 ± 8.5 years) performed concentric and eccentric contractions with constant external loading at different levels. Surface electromyography (EMG) and mechanomyography (MMG) were recorded from BB. 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. Variations in total intensity, first principal component (PCI), and the angle θ formed by first component (PCI) and second component (PCII) loading scores were explained in terms of MU recruitment patterns and elbow angles. Elbow angle had a significant effect on dynamic concentric and eccentric contractions. The EMG total intensity was greater for concentric than for eccentric contractions in the present study. MMG total intensity, however, was lower during concentric than during eccentric contractions. In addition, there was no significant difference in θ between concentric and eccentric contractions for both EMG and MMG. Selective recruitment of fast MUs from BB muscle during eccentric muscle contractions was not found in the present study.

Coordination of respiratory muscles assessed by means of nonlinear forecasting of demodulated myographic signals

Pulmonary diseases such as obstructive sleep apnea syndrome (OSAS) affect function of respiratory muscles. Individuals with OSAS suffer intermittent collapse of the upper airways during sleep due to unbalanced forces generated by the contraction of the diaphragm and upper airway dilator muscles. Respiratory rhythm and pattern generation can be described via nonlinear or coupled oscillators; therefore, the resulting activation of different respiratory muscles may be related to complex nonlinear interactions. The aims of this work were: to evaluate locally linear models for fitting and prediction of demodulated myographic signals from respiratory muscles; and to analyze quantitatively the influence of a pulmonary disease on this nonlinear forecasting related to low and moderate levels of respiratory effort. Electromyographic and mechanomyographic signals from three respiratory muscles (genioglossus, sternomastoid and diaphragm) were recorded in OSAS patients and controls while awake during an increased respiratory effort. Variables related to auto and cross prediction between muscles were calculated from the r 2 coefficient and the estimation of residuals, as functions of prediction horizon. In general, prediction improved linearly with higher levels of effort. A better prediction between muscle activities was obtained in OSAS patients when using genioglossus as the predictor signal. The prediction was significant for more than two respiratory cycles in OSAS patients compared to only a half cycle in controls. It could be concluded that nonlinear forecasting applied to genioglossus coupling with other muscles provides a promising assessment to monitor pulmonary diseases.

Wavelet-based intensity analysis of mechanomyographic signals during single-legged stance following fatigue

The von Tscharner (2000) “intensity analysis” describes the power of a non-stationary signal as a function of both frequency and time. The present study applied a version of this intensity analysis that utilizes Morlet wavelets as a means of gaining insight into the application of this technique as alternative to power spectral analysis for the evaluation of postural control strategy during the single-legged stance and to examine the effects of fatigue. Ten subjects (gender balanced, age: 25 ± 3 years; height: 169.4 ± 11.7 cm; weight: 79.0 ± 16.9 kg) participated in two trials consisting of five 15-s dominant-leg stances. Three-uniaxial accelerometers were fixed to the surface of the dominant leg corresponding to VM, VL, SOL, and MMG was recorded at a sampling rate of 1000 Hz. Signals were later analyzed using a variation of the von Tscharner intensity analysis consisting of a filter bank of 11 Morlet wavelets (range: 2.1–131.1 Hz). Two Wingate anaerobic tests (WAnT) separated by a 2-min rest were performed to introduce fatigue. Repeated measures ANOVAs showed significant effects for time, gender, trial, and wavelet ( p < 0.001) and significant interactions for muscle by wavelet, gender by trial, trial by wavelet, and gender by trial by wavelet ( p < 0.001). Peak total MMG intensity (mean ± SD) was higher in males than females and higher following fatiguing exercise preWAnT (squared m s −2): 42.6 ± 4.5 vs. 19.2 ± 2.3; postWAnT (squared m s −2): 90.4 ± 9.1 vs. 28.4 ± 2.8. Peak total MMG intensity was compressed to the lower frequencies surrounding ∼12 Hz, corresponding to what might be considered physiologic tremor, and a lower peak at ∼42 Hz was most prominent in SOL. The intensity analysis is a useful tool in exploring postural control and in studying the effects of fatigue on the mechanical properties of skeletal muscle.

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.

Asymmetry of Force Fluctuation during Low and Moderate Intensity Isometric Knee Extensions

The purpose of this study was to investigate the asymmetry of force fluctuation during isometric knee extension at low and moderate intensities. 11 healthy men (M age = 21 yr., SD = 1) performed unilateral force matching tasks; sustained isometric knee extension at 20% and 30% maximal voluntary contraction (MVC). During the tasks, a mechanomyogram was measured by an accelerometer arrangement placed on the vastus lateralis. Although force fluctuation was not significantly different between the two legs at 20% MVC, it was higher in the left (weaker) leg than in the right (stronger) leg at 30% MVC. A significant difference in mean power frequency of the mechanomyographic signal between the two legs was also observed only at 30% MVC. These results suggest that the asymmetry of force fluctuation during isometric knee extension was not statistically significant at low intensity; however, it was significant at moderate intensity. These differences in force fluctuation between intensities might be influenced by different motor-unit firing rates in active muscle.

Acute Effects of Caffeine on Strength and Muscle Activation of the Elbow Flexors

PURPOSE: To examine the effects of caffeine on maximal strength, rate of torque development (RTD), electromyographic (EMG) and mechanomyographic (MMG) signals, and electromechanical (EMD) and phonomechanical (PMD) delay. METHODS: Fourteen male volunteers came to the laboratory four times. Visit one served as a familiarization visit. During visit two, subjects ingested a randomly assigned drink, either with or without caffeine (0, 5, or 10 mg·kg -1 of body mass), and performed three maximal isometric muscle actions of the elbow flexors sixty minutes after ingestion. Maximal strength and RTD were recorded. EMG and MMG amplitude and frequency, and EMD and PMD were measured from the biceps brachii. Visit three was identical to visit two, but one of the remaining two drinks not administered was assigned. Visit four followed the same procedures as visit three, but the one remaining drink not assigned was administered. RESULTS: There were no significant differences for maximal strength, RTD, EMG amplitude and frequency, MMG amplitude, EMD and PMD. MMG frequency was significantly lower after the ingestion of the 5 mg·kg -1 of body mass of caffeine drink compared to the placebo trial. This was most likely an isolated findinig as MMG frequency was the only variable to have a significant difference across all trials. CONCLUSIONS: The results suggested that ingestion of either 5 or 10 mg·kg -1 of body mass of caffeine does not provide ergogenic effects during isometric muscle actions of the elbow flexors.

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.