Uncovering patterns of forearm muscle activity using multi-channel mechanomyography

A coordinated activation of distal forearm muscles allows the hand and fingers to be shaped during movement and grasp. However, little is known about how the muscle activation patterns are reflected in multi-channel mechanomyogram (MMG) signals. The purpose of this study is to determine if multi-site MMG signals exhibit distinctive patterns of forearm muscle activity. MMG signals were recorded from forearm muscle sites of nine able-bodied participants during hand movement. By using 14 features selected by a genetic algorithm and classified by a linear discriminant analysis classifier (LDA), we show that MMG patterns are specific and consistent enough to identify 7 ± 1 hand movements with an accuracy of 90 ± 4%. MMG-based movement recognition required a minimum of three recording sites. Further, by classifying five classes of contraction patterns with 98 ± 3% accuracy from MMG signals recorded from the residual limb of an amputee participant, we demonstrate that MMG shows pattern-specificity even in the absence of typical musculature. Multi-site monitoring of the RMS of MMG signals is suggested as a method of estimating the relative contributions of muscles to motor tasks. The patterns in MMG facilitate our understanding of the mechanical activity of muscles during movement.     

Within-day and between-days reliability of quadriceps isometric muscle fatigue using mechanomyography on healthy subjects

This study aimed to examine within-day and between-days intratester reliability of mechanomyography (MMG) in assessing muscle fatigue. An accelerometer was used to detect the MMG signal from rectus femoris. Thirty one healthy subjects (15 males) with no prior knee problems initially performed three maximum voluntary contractions (MVCs) using an ISOCOM dynamometer. After 10 min rest, subjects performed a fatiguing protocol in which they performed three isometric knee extensions at 75% MVC for 40 s. The fatiguing protocol was repeated on two other days, two to four days apart for between-days reliability. MMG activity was determined by overall root mean squared amplitude (RMS), mean power frequency (MPF) and median frequency (MF) during a 40 s contraction. RMS, MPF and MF linear regression slopes were also analysed. Intraclass Correlation Coefficients (ICC); ICC1,1 and ICC1,2 were used to assess within-day reliability and between-days reliability respectively. Standard error of measurement (SEM) and smallest detectable difference (SDD) described the within-subjects variability. MMG fatigue measures using linear regression slopes showed low reliability and large between-days error (ICC1,2 = 0.43–0.46; SDD = 306.0–324.8% for MPF and MF slopes respectively). Overall MPF and MF, on the other hand, were reliable with high ICCs and lower SDDs compared to linear slopes (ICC1,2 = 0.79–0.83; SDD = 21.9–22.8% for MPF and MF respectively). ICC1,2 for overall MMG RMS and linear RMS slopes were 0.81 and 0.66 respectively; however, the SDDs were high (56.4% and 268.8% respectively). The poor between-days reliability found in this study suggests caution in using MMG RMS, MPF and MF and their corresponding slopes in assessing muscle fatigue.     

The mechanomyography of persons after stroke during isometric voluntary contractions.

This study was to investigate the properties of mechanomyography (MMG), or muscle sound, of the paretic muscle in the affected side of hemiplegic subjects after stroke during isometric voluntary contractions, in comparison with those from the muscle in the unaffected side of the hemiplegic subjects and from the healthy muscle of unimpaired subjects. MMG and electromyography (EMG) signals were recorded simultaneously from the biceps brachii muscles of the dominant arm of unimpaired subjects (n=5) and the unaffected and affected arms of subjects after stroke (n=8), when performing a fatiguing maximal voluntary contraction (MVC) associated with the decrease in elbow flexion torque, and then submaximal elbow flexions at 20%, 40%, 60% and 80% MVCs. The root mean squared (RMS) values, the mean power frequencies (MPF, in the power density spectrum, PDS) of the EMG and MMG, and the high frequency rate (HF-rate, the ratio of the power above 15Hz in the MMG PDS) were used for the analysis. The MMG RMS decreased more slowly during the MVC in the affected muscle compared to the healthy and unaffected muscles. A transient increase could be observed in the MMG MPFs from the unaffected and healthy muscles during the MVC, associated with the decrease in their simultaneous EMG MPFs due to the muscular fatigue. No significant variation could be seen in the EMG and MMG MPFs in the affected muscles during the MVC. The values in the MPF and HF-rate of MMG from the affected muscles were significantly lower than those from the healthy and unaffected muscles (P<0.05) at the high contraction level (80% MVC). Both the MMG and EMG RMS values in the healthy and unaffected groups were found to be significantly higher than the affected group (P<0.05) at 60% and 80% MVCs. These observations were related to an atrophy of the fast-twitch fibers and a reduction of the neural input in the affected muscles of the hemiplegic subjects. The results in this study suggested MMG could be used as a complementary to EMG for the analysis on muscular characteristics in subjects after stroke.     

Uncovering chaotic structure in mechanomyography signals of fatigue biceps brachii muscle

The mechanomyography (MMG) signal reflects mechanical properties of limb muscles that undergo complex phenomena in different functional states. We undertook the study of the chaotic nature of MMG signals by referring to recent developments in the field of nonlinear dynamics. MMG signals were measured from the biceps brachii muscle of 5 subjects during fatigue of isometric contraction at 80% maximal voluntary contraction (MVC) level. Deterministic chaotic character was detected in all data by using the Volterra–Wiener–Korenberg model and noise titration approach. The noise limit, a power indicator of the chaos of fatigue MMG signals, was 22.20±8.73. Furthermore, we studied the nonlinear dynamic features of MMG signals by computing their correlation dimension D₂, which was 3.35±0.36 across subjects. These results indicate that MMG is a high-dimensional chaotic signal and support the use of the theory of nonlinear dynamics for analysis and modeling of fatigue MMG signals.     

Comparison between muscle activation measured by electromyography and muscle thickness measured using ultrasonography for effective muscle assessment

In this study, we aimed to compare the intrarater reliability and validity of muscle thickness measured using ultrasonography (US) and muscle activity via electromyography (EMG) during manual muscle testing (MMT) of the external oblique (EO) and lumbar multifidus (MF) muscles. The study subjects were 30 healthy individuals who underwent MMT at different grades. EMG was used to measure the muscle activity in terms of ratio to maximum voluntary contraction (MVC) and root mean square (RMS) metrics. US was used to measure the raw muscle thickness, the ratio of muscle thickness at MVC, and the ratio of muscle thickness at rest. One examiner performed measurements on each subject in 3 trials. The intrarater reliabilities of the % MVC RMS and raw RMS metrics for EMG and the % MVC thickness metrics for US were excellent (ICC = 0.81–0.98). There was a significant difference between all the grades measured using the % MVC thickness metric (p < 0.01). Further, this % MVC thickness metric of US showed a significantly higher correlation with the EMG measurement methods than with the others (r = 0.51–0.61). Our findings suggest that the % MVC thickness determined by US was the most sensitive of all methods for assessing the MMT grade.     

The effects of motion artifact on mechanomyography: A comparative study of microphones and accelerometers

Mechanomyography (MMG) is an important kinesiological tool and potential communication pathway for individuals with disabilities. However, MMG is highly susceptible to contamination by motion artifact due to limb movement. A better understanding of the nature of this contamination and its effects on different sensing methods is required to inform robust MMG sensor design. Therefore, in this study, we recorded MMG from the extensor carpi ulnaris of six able-bodied participants using three different co-located condenser microphone and accelerometer pairings. Contractions at 30% MVC were recorded with and without a shaker-induced single-frequency forearm motion artifact delivered via a custom test rig. Using a signal-to-signal-plus-noise-ratio and the adaptive Neyman curve-based statistic, we found that microphone-derived MMG spectra were significantly less influenced by motion artifact than corresponding accelerometer-derived spectra (p ? 0.05). However, non-vanishing motion artifact harmonics were present in both spectra, suggesting that simple bandpass filtering may not remove artifact influences permeating into typical MMG bands of interest. Our results suggest that condenser microphones are preferred for MMG recordings when the mitigation of motion artifact effects is important.     

Reliability of mechanomyography and triaxial accelerometry in the assessment of balance

Reliability of high-resolution accelerometery (HRA) and mechanomyography (MMG) was evaluated for the assessment of single-leg balance. Subjects (5M/5F, 25 ± 3 yr; 169.4 ± 11.7 cm; 79.0 ± 16.9 kg) participated in fifteen (three randomized bouts of five repetitions) 15-s dominant leg stances. A single HRA was fixed superficial to L3/L4 segment to capture motions relative to the center-of-mass, and three-uniaxial accelerometers were fixed on the surface of the dominant leg correspondent to the vastus medialis (VM), vastus lateralis (VL), and soleus (SOL) muscles to record MMG. Triaxial signals from the HRA (s.r. = 625 Hz) were streamed to a base station, simultaneously with MMG (s.r. = 1000 Hz). Signals were sampled, recorded and later analyzed. HRAs were recorded in g’s for vertical (VT), medial/lateral (ML), anterior/posterior (AP) directions, and resultant (RES) scalar. Intraclass correlation coefficients (ICC) were computed for each and Pearson’s r was calculated for the relationships between MMG and HRA (α ? 0.05). Except for RES (ICC = 0.36), all measures demonstrated moderately strong reliability (ICC = 0.75, 0.73, 0.63, 0.87, 0.89, and 0.86 for VM, VL, SOL, VT, ML, and AP, respectively). HRA and MMG provide reliable information pertaining to balance, and may have application in evaluating postural control and stability.     

Acute effects of direct inhibitory pressure over the biceps brachii myotendinous junction on skeletal muscle activation and force output

Force (F) reduction is reported with myotendinous junction (MTJ) manipulation. Autogenic inhibition reflex (AIR) activation is supposed to be the main mechanism. Still, its role remains unclear. The study aimed at assessing the effects of MTJ direct inhibitory pressure (DIP) on neuromuscular activation and F in the elbow flexor (agonist) and extensor (antagonist) muscles. After maximum voluntary contraction (MVC) assessment, thirty-five participants randomly performed submaximal contractions at 20, 40, 60, and 80% MVC. Electromyographic (EMG), mechanomyographic (MMG), and F signals were recorded. Protocol was repeated under (i) DIP (10-s pressure on the biceps brachii MTJ) with the elbow at 120° (DIP₁₂₀), (ii) DIP with the elbow at 180° (DIP₁₈₀), and (iii) without DIP (Ctrl). Electromechanical delay (EMD) components, EMG and MMG root mean square (RMS), and rate of force development (RFD) were calculated. Independently from the angle, DIP induced decrements in MVC, RFD, and RMS of EMG and MMG signals and lengthened the EMD components in agonist muscles (P < 0.05). The DIP-induced decrease in F output of the agonist muscles seems to be possibly due to a concomitant impairment of the neuromuscular activation and a transient decrease in stiffness. After DIP, the antagonist muscle displayed no changes; therefore, the intervention of AIR remains questionable.     

Non-invasive characterization of single motor unit electromyographic and mechanomyographic activities in the biceps brachii muscle.

The aim of the study was to investigate amplitude and frequency content of single motor unit (MU) electromyographic (EMG) and mechanomyographic (MMG) responses. Multi-channel surface EMG and MMG signals were detected from the dominant biceps brachii muscle of 10 volunteers during isometric voluntary contractions at 20%, 50%, and 80% of the maximal voluntary contraction (MVC) force. Each contraction was performed three times in the experimental session which was repeated in three non-consecutive days. Single MU action potentials were identified from the surface EMG signals and their times of occurrence used to trigger the averaging of the MMG signal. At each contraction level, the MUs with action potentials of highest amplitude were identified. Single MU EMG and MMG amplitude and mean frequency were estimated with normalized standard error of the mean within subjects (due to repetition of the measure in different trials and experimental sessions) smaller than 15% and 7%, respectively, in all conditions. The amplitude of the action potentials of the detected MUs increased with increasing force (mean +/- SD, 244 +/- 116 microV at 20% MVC, and 1426 +/- 638 microV at 80% MVC; P < 0.001) while MU MMG amplitude increased from 20% to 50% MVC (40.5 +/- 20.9 and 150 +/- 88.4 mm/s(2), respectively; P<0.001) and did not change significantly between 50% and 80% MVC (129 +/ -82.7 mm/s(2) at 80% MVC). MU EMG mean frequency decreased with contraction level (20% MVC: 97.2 +/- 13.9 Hz; 80% MVC: 86.2 +/- 11.4 Hz; P < 0.001) while MU MMG mean frequency increased (20% MVC: 33.2 +/- 6.8 Hz; 80% MVC: 40.1 +/- 6.1 Hz; P < 0.001). EMG peak-to-peak amplitude and mean frequency of individual MUs were not correlated with the corresponding variables of MMG at any contraction level.     

Effect of conditioning contraction intensity on postactivation potentiation is muscle dependent

We aimed to examine whether the influence of conditioning contraction intensity on the extent of postactivation potentiation (PAP) is muscle dependent. Eleven healthy males performed both thumb adduction and plantar flexion as a conditioning contraction. The conditioning contraction intensities were set at 20%, 40%, 60%, 80%, or 100% of the maximal voluntary isometric contraction (MVC).Before and after the conditioning contraction, twitch torque was measured for the respective joint to calculate the extent of PAP. In plantar flexion, the extent of PAP became significantly larger as the conditioning contraction intensity increased up to 80% MVC (p < 0.05). In contrast, the extent of PAP in thumb adduction increased significantly only up to 60% MVC (p < 0.05), but not at higher intensities.These results indicate that the influence of the conditioning contraction intensity on the extent of PAP is muscle dependent. Our results suggest that a conditioning contraction with submaximal intensity can sufficiently evoke sizable PAP in the muscle where most of muscle fibers are recruited at submaximal intensities, thereby attenuating muscle fatigue induced by the conditioning contraction.     

Concentric and eccentric isokinetic muscle activity separated by paired pattern classification of wavelet transformed mechanomyograms

It was hypothesized that concentric and eccentric isokinetic muscle actions should yield detectable differences in the mechanomyograms, which may reflect properties of the contraction and relaxation phases of the muscles. A paired pattern classification technique was adapted to determine whether wavelet transformed mechanomyograms from the three superficial quadriceps muscles were different during maximal concentric and eccentric isokinetic muscle actions. Mechanomyograms for this study were recorded from eleven healthy men (mean ± SD age = 20.1 ± 1.1 yrs) who performed maximal concentric and eccentric isokinetic muscle actions of the dominant leg extensors at a velocity of 30° s^?1. The results indicated that the paired pattern classification accurately classified the MMG intensity patterns in approximately 94% of the cases as being from a concentric or eccentric movement. Thus, it can be concluded that the differences in the intensity patterns recorded from concentric and eccentric muscle actions were significant. These findings indicated that the combined MMG wavelet analysis and pattern classification techniques could potentially be useful in situations where muscle activity during concentric muscle actions must be distinguished from that during eccentric muscle actions.     

Voluntary low-force contraction elicits prolonged low-frequency fatigue and changes in surface electromyography and mechanomyography.

Controversies exist regarding objective documentation of fatigue development with low-force contractions. We hypothesized that non-exhaustive, low-force muscle contraction may induce prolonged low-frequency fatigue (LFF) that in the subsequent recovery period is detectable by electromyography (EMG) and in particular mechanomyography (MMG) during low-force rather than high-force test contractions. Seven subjects performed static wrist extension at 10% maximal voluntary contraction (MVC) for 10 min (10%MVC10 min). Wrist force response to electrical stimulation of extensor carpi radialis muscle (ECR) quantified LFF. EMG and MMG were recorded from ECR during static test contractions at 5% and 80% MVC. Electrical stimulation, MVC, and test contractions were performed before 10%MVC10 min and at 10, 30, 90 and 150 min recovery. In spite of no changes in MVC, LFF persisted up to 150 min recovery but did not develop in a control experiment omitting 10%MVC10 min. In 5% MVC tests significant increase was found in time domain of EMG from 0.067+/-0.028 mV before 10%MVC10 min to 0.107+/-0.049 and 0.087+/-0.05 mV at 10 and 30 min recovery, respectively, and of the MMG from 0.054+/-0.039 ms(-2) to 0.133+/-0.104 and 0.127+/-0.099 ms(-2), respectively. No consistent changes were found in 80% MVC tests. In conclusion, non-exhaustive low-force muscle contraction resulted in prolonged LFF that in part was identified by the EMG and MMG signals.     

Detection of surface electromyography recording time interval without muscle fatigue effect for biceps brachii muscle during maximum voluntary contraction

The effects of fatigue on maximum voluntary contraction (MVC) parameters were examined by using force and surface electromyography (sEMG) signals of the biceps brachii muscles (BBM) of 12 subjects. The purpose of the study was to find the sEMG time interval of the MVC recordings which is not affected by the muscle fatigue. At least 10 s of force and sEMG signals of BBM were recorded simultaneously during MVC. The subjects reached the maximum force level within 2 s by slightly increasing the force, and then contracted the BBM maximally. The time index of each sEMG and force signal were labeled with respect to the time index of the maximum force (i.e. after the time normalization, each sEMG or force signal’s 0 s time index corresponds to maximum force point). Then, the first 8 s of sEMG and force signals were divided into 0.5 s intervals. Mean force, median frequency (MF) and integrated EMG (iEMG) values were calculated for each interval. Amplitude normalization was performed by dividing the force signals to their mean values of 0 s time intervals (i.e. ?0.25 to 0.25 s). A similar amplitude normalization procedure was repeated for the iEMG and MF signals. Statistical analysis (Friedman test with Dunn’s post hoc test) was performed on the time and amplitude normalized signals (MF, iEMG). Although the ANOVA results did not give statistically significant information about the onset of the muscle fatigue, linear regression (mean force vs. time) showed a decreasing slope (Pearson-r = 0.9462, p < 0.0001) starting from the 0 s time interval. Thus, it might be assumed that the muscle fatigue starts after the 0 s time interval as the muscles cannot attain their peak force levels. This implies that the most reliable interval for MVC calculation which is not affected by the muscle fatigue is from the onset of the EMG activity to the peak force time. Mean, SD, and range of this interval (excluding 2 s gradual increase time) for 12 subjects were 2353, 1258 ms and 536–4186 ms, respectively. Exceeding this interval introduces estimation errors in the maximum amplitude calculations of MVC–sEMG studies for BBM. It was shown that, simultaneous recording of force and sEMG signals was required to calculate the maximum amplitude of the MVC–sEMG more accurately.     

Mechanomyogram from the different heads of the quadriceps muscle during incremental knee extension

To investigate the time- and frequency-domain responses of mechanomyograms (MMGs) during the progressive fatigue induced by intermittent incremental contractions, a surface MMG was obtained from the three muscle heads of the quadriceps muscle in seven subjects while they performed isometric knee extensions lasting 7.6 min. Isometric intermittent incremental contractions started at 1% of the maximal voluntary contraction (MVC) for 3 s, with a 3-s relaxation period in between each contraction, and the contraction level was increased by 1% of MVC for every contraction (by 10% of MVC per min) up to exhaustion. Separate contractions with sufficient rest periods were also conducted to serve for the MMG characteristics without fatigue. The integrated MMG (iMMG) was linearly related to force in all of the muscles when fatigue was not involved. With regard to the incremental contractions, the relationship exhibited an ascending-descending shape, but the behavior was not the same for the individual muscle heads, especially for the rectus femoris muscle. A steep increase in the median frequency of MMG from around 60% of MVC corresponded to a decrease in iMMG. These results suggest that analysis of MMG in the time- and frequency-domain during an incremental protocol is a useful way of characterizing the motor unit recruitment strategy and fatigue properties of individual muscles. Accepted: 19 March 1998     

Similar response of agonist and antagonist muscles after eccentric exercise revealed by electromyography and mechanomyography.

The purpose of this study was to investigate the influence of eccentric contractions (ECC) on the biceps (BB) and triceps brachii (TB) muscles during maximal voluntary contraction (MVC) of elbow flexors using electrical (EMG) and mechanomyographical activities (MMG). Each of 18 male students performed 25 submaximal contractions (50% MVC) of the elbow flexors. Root mean square amplitude (RMS) and median frequency (MDF) were calculated for the EMG and MMG signals recorded during MVC. All measurements were taken before, immediately after, 24, 48, 72, and 120 h post-ECC from the BB and TB muscles. MVC was reduced by 34% immediately after exercise and did not return to the resting value within 120 h (P0.05). The EMG MDF decreased significantly (P< or =0.05) in both muscles after ECC. The MMG RMS at 24h, 48, 72 and 120 h post-ECC was significantly lower compared to that recorded immediately after ECC in both muscles (P< or =0.05). The present research showed that (i) there were similar changes in electrical and mechanical activities during MVC after submaximal ECC in agonist and antagonist muscles suggesting a common drive controlling the agonist and antagonist motoneuron pool, (ii) the ECC induced different changes in EMG than in MMG immediately after ECC and during 120 h of recovery that suggested an increased tremor and contractile impairments, i.e., reduced rate of calcium release from the sarcoplasmic reticulum (acute effect), and changes in motor control mechanisms of agonist and antagonist muscles, and increased muscle stiffness (chronic effect).     

Time/frequency events of surface mechanomyographic signals resolved by nonlinearly scaled wavelets

The purpose of this investigation is to introduce a wavelet analysis designed for analyzing short events reflecting bursts of muscle activity in non-stationary mechanomyographic (MMG) signals. A filter bank of eleven nonlinearly scaled wavelets that maintain the optimal combination of time and frequency resolution across the frequency range of MMG signals (5–100 Hz) was used for the analysis. A comparison with the short-time Fourier transform, Wigner-Ville transform and continuous wavelet transform using a test signal with known time–frequency characteristics showed that the MMG wavelet analysis resolved the intensity, timing, and frequencies of events in a more distinct way without overemphasizing high or low frequencies or generating interference terms. The analysis was used to process MMG signals from the vastus lateralis, rectus femoris, and vastus medialis muscles obtained during maximal concentric and eccentric isokinetic movements. Muscular events were observed that were precisely located in time and frequency in a muscle-specific way, thereby showing periods of synergistic contractions of the quadriceps muscles. The MMG wavelet spectra showed different spectral bands for concentric and eccentric isokinetic movements. In addition, the high and low frequency bands seemed to be activated independently during the isokinetic movement. What generates these bands is not yet known, however, the MMG wavelet analysis was able to resolve them, and is therefore applicable to non-stationary MMG signals.     

Relationships between skinfold thickness and electromyographic and mechanomyographic amplitude recorded during voluntary and non-voluntary muscle actions

IntroductionThe purpose of this study was to examine possible correlations between skinfold thicknesses and the a terms from the log-transformed electromyographic (EMG_RMS) and mechanomyographic amplitude (MMG_RMS)-force relationships, EMG M-Waves, and MMG gross lateral movements (GLM).MethodsForty healthy subjects performed a 6-s isometric ramp contraction from 5% to 85% of their maximal voluntary contraction with EMG and MMG sensors placed on the vastus lateralis (VL) and rectus femoris (RF). A single electrical stimulus was applied to the femoral nerve to record the EMG M-waves and MMG GLMs. Skinfold thickness was assessed at the site of each electrode. Pearson’s product correlation coefficients were calculated comparing skinfold thicknesses with the a terms from the log-transformed EMG_RMS-and MMG_RMS-force relationships, EMG M-waves, and MMG GLMs.ResultsThere were no significant cor1relations (p > 0.05) between the a terms and skinfold thicknesses for the RF and VL from the EMG_RMS and MMG_RMS-force relationships. However, there were significant correlations (p < 0.05) between skinfold thicknesses and the EMG M-waves and MMG GLMs for the RF (r = −0.521, −0.376) and VL (r = −0.479, −0.484).DiscussionRelationships were only present between skinfold thickness and the amplitudes of the EMG and MMG signals during the non-voluntary muscle actions.     

Effect of mental fatigue on induced tremor in human knee extensors

In this study, the effects of mental fatigue on mechanically induced tremor at both a low (3–6 Hz) and high (8–12 Hz) frequency were investigated. The two distinct tremor frequencies were evoked using two springs of different stiffness, during 20 s sustained contractions of the knee extensor muscles at 30% maximum voluntary contraction (MVC) before and after 100 min of a mental fatigue task, in 12 healthy (29 ± 3.7 years) participants. Mental fatigue resulted in a 6.9% decrease in MVC and in a 9.4% decrease in the amplitude of the agonist muscle EMG during sustained 30% MVC contractions in the induced high frequency only. Following the mental fatigue task, the coefficient of variation and standard deviation of the force signal decreased at 8–12 Hz induced tremor by 31.7% and 35.2% respectively, but not at 3–6 Hz induced tremor. Similarly, the maximum value and area underneath the peak in the power spectrum of the force signal decreased by 55.5% and 53.1% respectively in the 8–12 Hz range only. In conclusion, mental fatigue decreased mechanically induced 8–12 Hz tremor and had no effect on induced 3–6 Hz tremor. We suggest that the reduction could be attributed to the decreased activation of the agonist muscles.     

Innervation zone shift at different levels of isometric contraction in the biceps brachii muscle

Experiments were carried out to examine whether innervation zone (IZ) location remains stable at different levels of isometric contraction in the biceps brachii muscle (BB), and to determine how the proximity of the IZ affects common surface electromyography (sEMG) parameters. Twelve subjects performed maximal (MVC) and submaximal voluntary isometric contractions at 10%, 20%, 30%, 40%, 50% and 75% of MVC. sEMG signals were recorded with a 13 rows × 5 columns grid of electrodes from the short head of BB. The IZ shifted in the proximal direction by up to 2.4 cm, depending upon the subject and electrode column. The mean shift of all the columns was 0.6 ± 0.4 cm (10% vs. 100% MVC, P < 0.001). This shift biased the average values of mean frequency (+21.8 ± 9.9 Hz, P < 0.001), root mean square (?0.16 ± 0.15 mV, P < 0.05) and conduction velocity (?1.15 ± 0.93 m/s, P < 0.01) in the channels immediately proximal to the IZ. The shift in IZ could be explained by shortening of the muscle fibers, and thus lengthening of the (distal) tendon due to increasing force. These results underline the importance of individual investigation of IZ locations before the placement of sEMG electrodes, even in isometric contractions.     

High frequency vibration conditioning stimulation centrally reduces myoelectrical manifestation of fatigue in healthy subjects

ObjectiveVibration conditioning has been adopted as a tool to improve muscle force and reduce fatigue onset in various rehabilitation settings. This study was designed to asses if high frequency vibration can induce some conditioning effects detectable in surface EMG (sEMG) signal; and whether these effects are central or peripheral in origin.Design300 Hz vibration was applied for 30 min during 5 consecutive days, to the right biceps brachii muscle of 10 healthy males aged from 25 to 50 years. sEMG was recorded with a 16 electrode linear array placed on the skin overlying the vibrated muscle. The test protocol consisted of 30% and 60% maximal voluntary contraction (MVC) as well as involuntary (electrically elicited) contractions before and after treatment.ResultsNo statistically significant differences were found between PRE and POST vibration conditioning when involuntary stimulus-evoked contraction and 30% MVC were used. Significant differences in the initial values and rates of change of muscle fibre conduction velocity were found only at 60% MVC.Conclusions300 Hz vibration did not induce any peripheral changes as demonstrated by the lack of differences when fatigue was electrically induced. Differences were found only when the muscle was voluntarily fatigued at 60% MVC suggesting a modification in the centrally driven motor unit recruitment order, and interpreted as an adaptive response to the reiteration of the vibratory conditioning.