A comparative study of simultaneous vibromyography and electromyography with active human quadriceps

Detecting subtle yet fast skeletal muscle contractions with ultrasoft and durable graphene-based cellular materials

Human bodily movements are primarily controlled by the contractions of skeletal muscles. Unlike joint or skeletal movements that are generally performed in the large displacement range, the contractions of the skeletal muscles that underpin these movements are subtle in intensity yet high in frequency. This subtlety of movement makes it a formidable challenge to develop wearable and durable soft materials to electrically monitor such motions with high fidelity for the purpose of, for example, muscle/neuromuscular disease diagnosis. Here we report that an intrinsically fragile ultralow-density graphene-based cellular monolith sandwiched between silicone rubbers can exhibit a highly effective stress and strain transfer mechanism at its interface with the rubber, with a remarkable improvement in stretchability (>100%). In particular, this hybrid also exhibits a highly sensitive, broadband-frequency electrical response (up to 180 Hz) for a wide range of strains. By correlating the mechanical signal of muscle movements obtained from this hybrid material with electromyography, we demonstrate that the strain sensor based on this hybrid material may provide a new, soft and wearable mechanomyography approach for real-time monitoring of complex neuromuscular–skeletal interactions in a broad range of healthcare and human–machine interface applications. This work also provides a new architecture-enabled functional soft material platform for wearable electronics.

Effect of warm-up and muscle fatiguing exercise on knee joint sounds in motion by vibroarthrography: A randomized crossover trial

Vibroarthrography measures joint sounds caused by sliding of the joint surfaces over each other. and can be affected by joint health, load and type of movement. Since both warm-up and muscle fatigue lead to local changes in the knee joint (e.g., temperature increase, lubrication of the joint, muscle activation), these may impact knee joint sounds. Therefore, this study investigates the effects of warm-up and muscle fatiguing exercise on knee joint sounds during an activity of daily living. Seventeen healthy, physically active volunteers (25.7 ± 2 years, 7 males) performed a control and an intervention session with a wash-out phase of one week. The control session consisted of sitting on a chair, while the intervention session contained a warm-up (walking on a treadmill) followed by a fatiguing exercise (modified sit-to-stand) protocol. Knee sounds were recorded by vibroarthrography (at the medial tibia plateau and at the patella) at three time points in each session during a sit-to-stand movement. The primary outcome was the mean signal amplitude (MSA, dB). Differences between sessions were determined by repeated measures ANOVA with intra-individual pre-post differences for the warm-up and for the muscle fatigue effect. We found a significant difference for MSA at the medial tibia plateau (intervention: mean 1.51 dB, standard deviation 2.51 dB; control: mean -1.28 dB, SD 2.61 dB; F = 9.5; p = .007; η² = .37) during extension (from sit to stand) after the warm-up. There was no significant difference for any parameter after the muscle fatiguing exercise (p > .05). The increase in MSA may mostly be explained by an increase in internal knee load and joint friction. However, neuromuscular changes may also have played a role. It appears that the muscle fatiguing exercise has no impact on knee joint sounds in young, active, symptom-free participants during sit to stand.

Obstructive sleep apnea detection using discrete wavelet transform-based statistical features

MOTIVATION AND OBJECTIVE

Obstructive sleep apnea (OSA) is a sleep disorder identified in nearly 10% of middle-aged people, which deteriorates the normal functioning of human organs, notably that of the heart. Furthermore, untreated OSA is associated with increased hypertension, diabetes, stroke, and cardiovascular diseases, thereby increasing the mortality risk. Therefore, early identification of sleep apnea is of significant interest.

METHOD

In this paper, an automated approach for OSA diagnosis using a single-lead electrocardiogram (ECG) has been reported. Three sets of features, namely moments of power spectrum density (PSD), waveform complexity measures, and higher-order moments, are extracted from the 1-min segmented ECG subbands obtained from discrete wavelet transform (DWT). Later, correlation-based feature selection with particle swarm optimization (PSO) search strategy is employed for getting an optimum feature vector. This process retained 18 significant features from initially computed 32 features. Finally, the acquired feature set is fed to different classifiers including, linear discriminant analysis, nearest neighbors, support vector machine, and random forest to perform per segment classification.

RESULTS

Experiments on the publicly available physionet single-lead ECG dataset show that the proposed approach using the random forest classifier effectively discriminates normal and OSA ECG signals. Specifically, our method achieved an accuracy of 89% and 90%, with 50-50 hold-out validation and 10-fold cross-validation, respectively. Besides, in both these validation scenarios, our method obtained 96% of the area under ROC. Importantly, our proposed approach provided better performance results than most of the existing methodologies.

Two-dimensional spatial error distribution of key tensiomyographic parameters

Tensiomyography detects the contraction time (Tc) and amplitude (Dm) of muscle belly thickening during maximal isometric twitch contraction. The assessment of both parameters is highly reliable; however, it seems that their calculation depends on the measurement point. The aim of the study was to determine spatial relative error distribution of Tc and/or Dm within a two-dimensional array of 27 (3 × 9) measurement points in comparison to the reference point (RP) in 12 male participants (22.5 ± 3.1 years). The RPs were determined as follows: in the biceps brachii (BB) at 50% of the humerus length; in the erector spinae (ES) at the height of the iliac crest; in the vastus lateralis (VL), vastus medalis (VM), and rectus femoris (RF) at 30%, 20%, and 50% of femur length above the patella, respectively. The surface area under the 3% relative error in Dm (BB: 4.0; VL: 3.8; VM; 8.2; RF: 6.2; ES: 2.4 cm²) was lower than in Tc (BB: 6.9; VL: 3.8; VM; 4.6; RF: 9.5; ES: 3.7 cm²), yielding merged values (BB: 3.9; VL: 3.7; VM; 4.8; RF: 5.1; ES: 2.4 cm²). Dm show twice as steep relative error rate when moving away from the RP in comparison to Tc, which seems to be less sensitive to spatial sensor positioning.

Noninvasive Assessment of Skeletal Muscle Myosin Heavy Chain Expression in Trained and Untrained Men

Fry, AC, Housh, TJ, Cramer, JB, Weir, JP, Beck, TW, Schilling, BK, Miller, JD, and Nicoll, JX. Noninvasive assessment of skeletal muscle myosin heavy chain expression in trained and untrained men. J Strength Cond Res 31(9): 2355-2362, 2017-Numerous conditions and types of physical activity (e.g., exercise, aging, and muscle-related diseases) can influence muscle fiber types and the proteins expressed. To date, muscle fibers can only be characterized by actually obtaining a tissue sample using the invasive muscle biopsy procedure. Mechanomyography (MMG) is the assessment of the vibration properties of contracting skeletal muscle and has been proposed as a possible noninvasive method for muscle fiber analysis. Therefore, the purpose of this project was to examine the feasibility of using MMG and muscle performance measures to noninvasively assess muscle fiber characteristics. Fifteen men (5 endurance-trained, 5 weight-trained, and 5 sedentary) provided muscle samples from their vastus lateralis muscle. These samples were analyzed for relative myosin heavy chain (MHC) protein expression, which is highly correlated with % muscle fiber type areas. Additionally, each subject performed several muscle performance tests, and MMG of the quadriceps was assessed during a knee extension exercise. Multiple regression was used to develop prediction equations for determining relative muscle content of MHC types I, IIa, and IIx. A combination of MMG and knee extension performance variables estimated types I, IIa, and IIx MHCs with approximately 80% accuracy. Although preliminary, these data suggest that muscle performance tests in addition to MMG assessments during a simple muscle performance task (knee extension) can be used to estimate muscle fiber type composition in a healthy male population. Such methods could ultimately be used to noninvasively monitor muscle health and fitness.

Mechanomyographic responses during recruitment curves in the soleus muscle

INTRODUCTION

In this study we examined relationships among mechanomyographic (MMG), electromyographic (EMG), and peak twitch torque (PTT) responses as well as test-retest reliability when recorded during recruitment curves in the soleus muscle.

METHODS

PTT, EMG (M-wave, H-reflex), and MMG responses were recorded during recruitment curves in 16 subjects (age 24 ± 2 years) on 2 separate days. The sum of the M-wave and H-reflex (M+H) was calculated. Correlations among variables and test-retest reliability were determined.

RESULTS

MMG was correlated with PTT (mean r = 0.93, range r = 0.59-0.99), the M-wave (0.95, 0.04-0.98), and M+H (0.91, 0.42-0.97), but was unrelated to the H-reflex (-0.06, -0.56 to 0.47). Reliability was consistently high among most variables, but normalizing to the maximum value improved MMG reliability and the minimum detectable change.

CONCLUSION

MMG responses predicted 86%-90% of the variability in PTT, M-wave, and M+H; thus, MMG may be a useful alternative for estimating twitch torque and maximal activation. Muscle Nerve 56: 107-116, 2017.

Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression

The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES) in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG) of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR) due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and test the model, the data were partitioned into training (70%) and testing (30%) subsets, respectively. The SVR estimation accuracy, based on the coefficient of determination (R²) between the actual and the estimated torque values was up to 94% and 89% during the training and testing cases, with root mean square errors (RMSE) of 9.48 and 12.95, respectively. The knee torque estimations obtained using SVR modelling agreed well with the experimental data from an isokinetic dynamometer. These findings support the realization of a closed-loop NMES system for functional tasks using MMG as the feedback signal source and an SVR algorithm for joint torque estimation.

Event Recognition for Contactless Activity Monitoring Using Phase-Modulated Continuous Wave Radar

OBJECTIVES

The use of remote sensing technologies such as radar is gaining popularity as a technique for contactless detection of physiological signals and analysis of human motion. This paper presents a methodology for classifying different events in a collection of phase modulated continuous wave radar returns. The primary application of interest is to monitor inmates where the presence of human vital signs amidst different, interferences needs to be identified.

METHODS

A comprehensive set of features is derived through time and frequency domain analyses of the radar returns. The Bhattacharyya distance is used to preselect the features with highest class separability as the possible candidate features for use in the classification process. The uncorrelated linear discriminant analysis is performed to decorrelate, denoise, and reduce the dimension of the candidate feature set. Linear and quadratic Bayesian classifiers are designed to distinguish breathing, different human motions, and nonhuman motions. The performance of these classifiers is evaluated on a pilot dataset of radar returns that contained different events including breathing, stopped breathing, simple human motions, and movement of fan and water.

RESULTS

Our proposed pattern classification system achieved accuracies of up to 93% in stationary subject detection, 90% in stop-breathing detection, and 86% in interference detection.

CONCLUSION

Our proposed radar pattern recognition system was able to accurately distinguish the predefined events amidst interferences.

SIGNIFICANCE

Besides inmate monitoring and suicide attempt detection, this paper can be extended to other radar applications such as home-based monitoring of elderly people, apnea detection, and home occupancy detection.

Mechanomyographic parameter extraction methods: an appraisal for clinical applications

The research conducted in the last three decades has collectively demonstrated that the skeletal muscle performance can be alternatively assessed by mechanomyographic signal (MMG) parameters. Indices of muscle performance, not limited to force, power, work, endurance and the related physiological processes underlying muscle activities during contraction have been evaluated in the light of the signal features. As a non-stationary signal that reflects several distinctive patterns of muscle actions, the illustrations obtained from the literature support the reliability of MMG in the analysis of muscles under voluntary and stimulus evoked contractions. An appraisal of the standard practice including the measurement theories of the methods used to extract parameters of the signal is vital to the application of the signal during experimental and clinical practices, especially in areas where electromyograms are contraindicated or have limited application. As we highlight the underpinning technical guidelines and domains where each method is well-suited, the limitations of the methods are also presented to position the state of the art in MMG parameters extraction, thus providing the theoretical framework for improvement on the current practices to widen the opportunity for new insights and discoveries. Since the signal modality has not been widely deployed due partly to the limited information extractable from the signals when compared with other classical techniques used to assess muscle performance, this survey is particularly relevant to the projected future of MMG applications in the realm of musculoskeletal assessments and in the real time detection of muscle activity.

Between-day reliability of a method for non-invasive estimation of muscle composition

Tensiomyography is a method for valid and non-invasive estimation of skeletal muscle fibre type composition. The validity of selected temporal tensiomyographic measures has been well established recently; there is, however, no evidence regarding the method's between-day reliability. Therefore it is the aim of this paper to establish the between-day repeatability of tensiomyographic measures in three skeletal muscles. For three consecutive days, 10 healthy male volunteers (mean±SD: age 24.6 ± 3.0 years; height 177.9 ± 3.9 cm; weight 72.4 ± 5.2 kg) were examined in a supine position. Four temporal measures (delay, contraction, sustain, and half-relaxation time) and maximal amplitude were extracted from the displacement-time tensiomyogram. A reliability analysis was performed with calculations of bias, random error, coefficient of variation (CV), standard error of measurement, and intra-class correlation coefficient (ICC) with a 95% confidence interval. An analysis of ICC demonstrated excellent agreement (ICC were over 0.94 in 14 out of 15 tested parameters). However, lower CV was observed in half-relaxation time, presumably because of the specifics of the parameter definition itself. These data indicate that for the three muscles tested, tensiomyographic measurements were reproducible across consecutive test days. Furthermore, we indicated the most possible origin of the lowest reliability detected in half-relaxation time.

Noninvasive estimation of myosin heavy chain composition in human skeletal muscle

PURPOSE

Information on muscle fiber type composition is of great importance in muscle physiology and athletic performance. Because there are only a few techniques available that noninvasively and accurately provide an estimate of muscle fiber type composition, the development of additional and alternative approaches is required.

METHODS

Twenty-seven participants (21 men, 6 women) with an average age of 43 ± 18 yr, height of 175 ± 7 cm, and mass of 74 ± 12 kg participated in the study. Delay, contraction, and half relaxation times were calculated from tensiomyographic radial twitch responses of the vastus lateralis muscle. Univariate and multiple linear regression analyses were used to correlate the proportion of myosin heavy chain I (%MHC-I) in a biopsy obtained from the same muscle with a single and all three radial twitch parameters.

RESULTS

Delay, contraction, and half relaxation times all correlated with %MHC-I (r = 0.612, 0.878, and 0.669, respectively, at P ≤ 0.001). When all three parameters were included in a multiple linear regression, the correlation with the %MHC-I was even better (R = 0.933, P < 0.001).

CONCLUSIONS

These data suggest that time parameters of the skeletal muscle mechanical radial twitch response, measured with a contact linear displacement sensor, can be used as an accurate noninvasive predictor of the %MHC-I in a muscle.

Vibromyographic quantification of voluntary isometric contractile force in the brachioradialis

This study investigated the ability of vibromyography (VMG) to accurately represent voluntary forearm muscle contractile force during attempted-isometric contraction of the brachioradialis. VMG signals were collected from the brachioradialis of healthy adult men (mean age, 26.6+/-9.8 years, N=24) during attempted-isometric contraction over a force range of 4.45 N to maximum sustained load. The VMG signals were decomposed using wavelet packet analysis techniques, and the corresponding wavelet packets were utilized in a multiple regression model for parameter reduction and identification of signal components which best correlated to muscle force. It was observed that just two wavelet components were sufficient to accurately predict muscle force (R2=0.984, P<0.0001). The signal force relationship observed is monotonic, though quadratic in form. More importantly, the wavelet data was able to predict absolute force output of the brachioradialis without normalization or prior knowledge of a subject's maximum voluntary force. These data show that VMG recordings are capable of providing a monotonic relationship between VMG signal and muscle force. Moreover, in contrast to EMG technology which can only provide relative force levels, VMG appears to be capable of reporting absolute force levels, an observation which is expected to lead to numerous applications in medicine and rehabilitation.

Gender Comparisons of Mechanomyographic Amplitude and Mean Power Frequency versus Isometric Torque Relationships

This study compared the patterns of mechanomyographic (MMG) amplitude and mean power frequency vs. torque relationships in men and women during isometric muscle actions of the biceps brachii. Seven men (mean age 23.9 ± 3.5 yrs) and 8 women (mean 21.0 ± 1.3 yrs) performed submaximal to maximal isometric muscle actions of the dominant forearm flexors. Following determination of the isometric maximum voluntary contraction (MVC), they randomly performed submaximal step muscle actions in 10% increments from 10% to 90% MVC. Polynomial regression analyses indicated that the MMG amplitude vs. isometric torque relationship for the men was best fit with a cubic model (R2= 0.983), where MMG amplitude increased slightly from 10% to 20% MVC, increased rapidly from 20% to 80% MVC, and plateaued from 80% to 100% MVC. For the women, MMG amplitude increased linearly (r2= 0.949) from 10% to 100% MVC. Linear models also provided the best fit for the MMG mean power frequency vs. isometric torque relationship in both the men (r2= 0.813) and women (r2= 0.578). The results demonstrated gender differences in the MMG amplitude vs. isometric torque relationship, but similar torque-related patterns for MMG mean power frequency. These findings suggested that the plateau in MMG amplitude at high levels of isometric torque production for the biceps brachii in the men, but not the women, may have been due to greater isometric torque, muscle stiffness, and/or intramuscular fluid pressure in the men, rather than to differences in motor unit activation strategies for modulating isometric torque production.

Mechanomyographic amplitude and mean power frequency versus torque relationships during isokinetic and isometric muscle actions of the biceps brachii

The purpose of this investigation was to determine the mechanomyographic (MMG) amplitude and mean power frequency (MPF) versus torque (or force) relationships during isokinetic and isometric muscle actions of the biceps brachii. Ten adults (mean +/- SD age = 21.6 +/- 1.7 years) performed submaximal to maximal isokinetic and isometric muscle actions of the dominant forearm flexors. Following determination of isokinetic peak torque (PT) and the isometric maximum voluntary contraction (MVC), the subjects randomly performed submaximal step muscle actions in 10% increments from 10% to 90% PT and MVC. Polynomial regression analyses indicated that MMG amplitude increased linearly with torque during both the isokinetic (r2 = 0.982) and isometric (r2 = 0.956) muscle actions. From 80% to 100% of isometric MVC, however, MMG amplitude appeared to plateau. Cubic models provided the best fit for the MMG MPF versus isokinetic (R2 = 0.786) and isometric (R2 = 0.940) torque relationships, although no significant increase in MMG MPF was found from 10% to 100% of isokinetic PT. For the isometric muscle actions, however, MMG MPF remained relatively stable from 10% to 50% MVC, increased from 50% to 80% MVC, and decreased from 80% to 100% MVC. The results demonstrated differences in the MMG amplitude and MPF versus torque relationships between the isokinetic and isometric muscle actions. These findings suggested that the time and frequency domains of the MMG signal may be useful for describing the unique motor control strategies that modulate dynamic versus isometric torque production.

Systematic characterisation of silicon-embedded accelerometers for mechanomyography

Silicon soft suction sockets (roll-on sleeves) currently used in passive prostheses for below-elbow amputees could also be used in externally powered prostheses, enhancing their functionality and comfort. However, as it is extremely difficult to hold currently used electromyography (EMG) sensors in place reliably within a silicon socket, an alternative measurement of muscular activity as the control input is necessary. Mechanomyography (MMG) is the epidermal measurement of the low-frequency vibrations produced by a contracting muscle. MMG sensors do not have to be in direct contact with the skin. Moreover, the embedding of sensors in the roll-on sleeve may also solve attachment issues, making sensor placement flexible. Therefore the objective was to determine the feasibility of recording MMG signals using silicon-embedded, micro-machined accelerometers. Fifteen embedded accelerometers were excited with predefined vibration patterns. The signal-to-noise ratio (SNR) and frequency response of each sample were measured and compared with those of non-embedded accelerometers. The SNR of embedded samples (approximately equal to 19 dB) was significantly higher than that of non-embedded samples (approximately equal to 12 dB), owing to the considerable mechanical damping effect of the silicon in the 300-900 Hz bandwidth (p=0.0028). This has implications for the application of silicon-embedded accelerometers for externally powered prosthesis control.

Reliability of the mechanomyogram detected with an accelerometer during voluntary contractions

The accelerometer is used for mechanomyogram (MMG) recordings of muscle contractions. Although the mechanical characteristics of other MMG transducers have been determined with reference to the accelerometer, mechanical aspects of the accelerometer itself, including the weight of the transducer, have not been verified. This study was designed to reinvestigate the mechanical variable of the MMG signal detected with an accelerometer, with reference to a laser distance sensor (LDS), and then to clarify the influence of the accelerometer weight on the MMG recording during muscle contractions. The study was performed during mechanical sinusoidal vibrations and during voluntary contractions of the quadriceps muscle. Maximum differences in the amplitude spectral density functions between the LDS signal and the double integral of the accelerometer signal were approximately 4 microm. The results verified that the MMG signal from the accelerometer accurately reflected the acceleration of body surface vibration. However, the MMG signal was gradually distorted when weight was added to the accelerometer: the addition of 4.0 g (total 6.0 g, including 2.0 g of accelerometer) substantially attenuated the MMG signal. The results suggest that the appropriate weight for the accelerometer should be less than 5.0 g for measurements of the quadriceps muscle and indicate that the transducer weight must be taken into account for accurate measurement of muscles of different sizes.

Mechanomyogram and electromyogram responses of upper limb during sustained isometric fatigue with varying shoulder and elbow postures

To investigate the behavior of mechanomyogram (MMG) and electromyogram (EMG) signals in the time and frequency domains during sustained isometric contraction, MMG and surface EMG were obtained simultaneously from four muscles: upper trapezius (TP), anterior deltoid (DL), biceps brachii (BB), and brachioradialis (BR) of 10 healthy male subjects. Experimental conditions consisted of 27 combinations of 9 postures [3 shoulder angles (SA): 0 degree, 30 degrees, 60 degrees and 3 elbow angles (EA): 120 degrees, 90 degrees, 60 degrees] and 3 contraction levels: 20%, 40%, and 60% of maximum voluntary contraction (MVC). Subjective evaluations of fatigue were also assessed using the Borg scale at intervals of 60, 30, and 10 sec at 20%, 40%, and 60% MVC tests, respectively. The mean power frequency (MPF) and root mean square (RMS) of both signals were calculated. The current study found clear and significant relationships among physiological and psychological parameters on the one hand and SA and EA on the other. EA's effect on MVC was found to be significant. SA had a highly significant effect on both endurance time and Borg scale. In all experimental conditions, significant correlations were found between the changes in MPF and RMS of EMG in BB with SA and EA (or muscle length). In all four muscles, MMG frequency content was two or three times lower than EMG frequency content. During sustained isometric contraction, the EMG signal showed the well-known shift to lower frequencies (a continuous decrease from onset to completion of the contraction). In contrast, the MMG spectra did not show any shift, although its form changed (generally remaining about constant). Throughout the contraction, increased RMS of EMG was found for all tests, whereas in the MMG signal, a significant progressive increase in RMS was observed only at 20% MVC in all four muscles. This supports the hypothesis that the RMS amplitude of the MMG signal produced during contraction is highly correlated with force production. Possible explanations for this behavioral difference between the MMG and EMG signals are discussed.

Influence of estimators of spectral density on the analysis of electromyographic and vibromyographic signals

Electromyographic (EMG) and vibromyographic (VMG) signals are related to electrical and mechanical muscle activity, respectively. It is known that variations in their frequency components are related to changes in muscle activity and fatigue. The aims of this study were: (1) to analyse the resolution, variance and bias of different estimations of power spectral density function (PSD); and (2) to evaluate the influence of the spectral estimation method on three indices calculated from the PSD of EMG and VMG signals: mean (f(m)) and median (f(c)) frequencies and the ratio of high and low frequency components (H/L ratio) to select the most suitable estimator. Myographic signals were recorded from the sternomastoid muscle, an accessory respiratory muscle, during breathing. For non-parametric methods, Welch periodograms and correlograms were analysed with different windows. Autoregressive (AR) moving average (MA) and ARMA models with different orders were evaluated in the parametric methods. The reproducibility of the results was also studied. Frequency indices, particularly the H/L ratio and f(c), changed considerably when varying the following parameters of the estimators: periodogram with segment durations longer than 150 ms in EMG and with any duration in VMG signals; correlogram with window length shorter than 10% of the total number of samples; and AR models with an order lower than 10, 20 and 40 in f(c), fm and H/L ratio, respectively, in both myographic signals.

Mean power frequency and amplitude of the mechanomyographic and electromyographic signals during incremental cycle ergometry

The purpose of this investigation was to determine the relationships for mechanomyographic (MMG) amplitude, MMG mean power frequency (MPF), electromyographic (EMG) amplitude, and EMG MPF versus power output during incremental cycle ergometry. Seventeen adults volunteered to perform an incremental test to exhaustion on a cycle ergometer. The test began at 50 W and the power output was increased by 30 W every 2 min until the subject could no longer maintain 70 rev min(-1). The MMG and EMG signals were recorded simultaneously from the vastus lateralis during the final 10 s of each power output and analyzed. MMG amplitude, MMG MPF, EMG amplitude, EMG MPF, and power output were normalized as a percentage of the maximal value from the cycle ergometer test. Polynomial regression analyses indicated that MMG amplitude increased (P<0.05) linearly across power output, but there was no change (P>0.05) in MMG MPF. EMG amplitude and MPF were fit best (P<0.05) with quadratic models. These results demonstrated dissociations among the time and frequency domains of MMG and EMG signals, which may provide information about motor control strategies during incremental cycle ergometry. The patterns for amplitude and frequency of the MMG signal may be useful for examining the relationship between motor-unit recruitment and firing rate during dynamic tasks.

Study of myographic signals from sternomastoid muscle in patients with chronic obstructive pulmonary disease

Analysis of the respiratory muscle activity is a promising technique for diagnosis of respiratory diseases, such as chronic obstructive pulmonary disease (COPD). The sternomastoid muscle (SMM) was selected to study the activity of respiratory muscles due to its accessibility in order to define a noninvasive analysis. The aims of this work are two: analyze the relationship between the SMM function and pulmonary obstruction, and study the influence of spectral estimator on frequency parameters related with the muscle activity. For the first goal, we propose the analysis of vibromyographic and electromyographic signals from the SMM to study the muscle function during two ventilatory tests. Activity of SMM was found by means of several indexes: root-mean-square (rms) values, mean and median frequencies, and ratio between high and low-frequency components. For the second goal, spectral analysis was performed by means of nonparametric methods: Correlogram and Welch periodogram, and parametric methods: autoregressive (AR), moving average (MA), and ARMA models. It is deduced that these indexes show muscle activity and certain fatigue of the SMM, whose muscle function depends on the level of pulmonary obstruction, and they depend a lot of spectral estimator being the more suitable an AR model with high order.

Gender comparisons of the mechanomyographic responses to maximal concentric and eccentric isokinetic muscle actions

PURPOSE

The purpose of this study was to determine whether there is a gender difference in the velocity-related patterns of mechanomyographic (MMG) responses to maximal isokinetic concentric (CON) and eccentric (ECC) muscle actions.

METHODS

Adult males (N = 15) and females (N = 16) performed maximal CON and ECC muscle actions of the leg extensors on a calibrated Cybex 6000 dynamometer at velocities of 30, 90, and 150 degrees.s-1. MMG was detected by a piezoelectric crystal contact sensor placed over the vastus lateralis muscle.

RESULTS

The results indicated that there were decreases in CON peak torque (PT) across velocities, while ECC PT remained constant with increasing velocity for both genders. MMG amplitude increased significantly (P < 0.05) with velocity in both the males and females for CON and ECC muscle actions. There was a gender difference in the velocity-related patterns of MMG responses to maximal isokinetic CON muscle actions; however, there was no gender difference in the pattern of ECC MMG responses.

CONCLUSIONS

The gender difference in CON MMG responses may be attributed to the greater percent decline in CON PT across velocity for the females than the males. In addition, the males displayed greater CON and ECC MMG amplitudes at all muscle action velocities than the females, possibly because of gender differences in muscle mass and/or thickness of the adipose tissue layer.

Technical aspects of mechnomyography recording with piezoelectric contact sensor

The piezoelectric contact sensor has been widely utilised in mechanomyography (MMG). The authors aim to clarify the mechanical variables (i.e. acceleration, velocity or displacement) reflected by the MMG signal detected with a piezoelectric contact sensor (PEC), and compare the results with those obtained simultaneously by an accelerometer (ACC). To measure the acceleration-frequency response, a mechanical sinusoidal excitation of 5 to 300 Hz at a constant magnitude of 0.01 G was applied to the two transducers. The acceleration-frequency response of the ACC transducer was confirmed to be almost flat. The PEC without any restriction of the transducer housing (including the combined seismic mass) demonstrated a similar response to the ACC transducer. The PEC transducer output with restricted housing decreased with increasing sinusoidal frequency and an attenuation slope of -40 dB/decade and phase angle of -180 degrees. The voluntary MMG signal during isometric knee extension was recorded simultaneously with the two transducers. The amplitude spectral density distribution of the MMG from the PEC transducer was narrow and the mean frequency was approximately one-half that obtained from the ACC transducer. The amplitude spectral density distribution with the PEC transducer resembled that of the double integral over time of the ACC transducer signal. The phase angle of the PEC transducer signal was different from that of the ACC transducer signal by approximately -180 degrees. These results suggest that the PEC transducer acts as a displacement meter of muscle vibration. In addition, differences in the MMG frequency components relating to the transducer type must be taken into consideration when investigating the mechanical activity of muscle.

MMG measurement: a high-sensitivity microphone-based sensor for clinical use

High-sensitivity human muscle-vibration measurement (MMG) sensor adapted to clinical use is presented. The muscle vibration phenomenon is modeled and investigated to optimize the measurement technique. The sensor uses an acoustic impedance adaptation technique to convert the skin surface vibration in terms of acoustic pressure, which is sensed by a microphone. The device is calibrated and gives the real amplitude of the vibration. It is also well fitted to measure other physiological vibrations in the 2 Hz-1 KHz range.

Mechanomyographic responses to maximal eccentric isokinetic muscle actions

The purpose of the present investigation was to examine the mechanomyographic (MMG) responses to maximal eccentric isokinetic muscle actions. Eight adult male volunteers [age 22 +/- 2 (SD) yr] performed maximal eccentric muscle actions of the leg extensors at 60, 90, 120, and 180 degrees /s on a Cybex 6000 isokinetic dynamometer. MMG was detected by a piezoelectric crystal contact sensor placed over the vastus lateralis muscle. Test-retest intraclass correlations ranged from R = 0.88 to 0.97 for peak torque and from R = 0.97 to 0.98 for root mean square MMG amplitude values. There was no significant (P > 0.05) velocity-related change in eccentric peak torque; however, there was a significant (P < 0.05) increase in MMG between 60 [119 +/- 44 (SE) mV] and 180 degrees/s (302 +/- 128 mV). These findings indicated a velocity-related dissociation between MMG and peak torque for maximal eccentric isokinetic muscle actions.

Assessment of muscular fatigue using vibromyography

The purpose of this study was to investigate the behavior of electromyographical (EMG) and vibromyographical (VMG) signals in the time and frequency domains during a fatigue protocol. EMG and VMG records were obtained from the rectus femoris (RF) and vastus lateralis (VL) muscles of 11 adult male subjects during sustained, isometric knee extensor contractions performed at 70% of maximal voluntary contraction (MVC). The average median frequencies of the power density spectra decreased during the fatigue protocol for the EMG (from 73 to 54 Hz for RF, and from 75 to 57 Hz for VL) and the VMG signals (from 40 to 19 Hz for RF, and from 25 to 12 Hz for VL). Raw EMG signals remained the same qualitatively throughout the fatigue protocol, whereas corresponding VMG records appeared to become "smoother." The results of this study indicate that the pronounced decrease in the high-frequency content of the VMG signal may be observed in the time domain as a "smoothing" of the signal, and thus, that the raw VMG records (which may be displayed readily online) can be used to assess qualitatively the onset and progression of muscular fatigue.

Adaptive cancellation of muscle contraction interference in vibroarthrographic signals

Vibroarthrography (VAG) is an innovative, objective, non-invasive technique for obtaining diagnostic information concerning the articular cartilage of a joint. Knee VAG signals can be detected using a contact sensor over the skin surface of the knee joint during knee movement such as flexion and/or extension. These measured signals, however, contain significant interference caused by muscle contraction that is required for knee movement. Quality improvement of VAG signals is an important subject, and crucial in computer-aided diagnosis of cartilage pathology. While simple frequency domain high-pass (or band-pass) filtering could be used for minimizing muscle contraction interference (MCI), it could eliminate possible overlapping spectral components of the VAG signals. In this work, an adaptive MCI cancellation technique is presented as an alternative technique for filtering VAG signals. Methods of measuring the VAG and reference signals (MCI) are described, with details on MCI identification, characterization, and step size optimization for the adaptive filter. The performance of the method is evaluated by simulated signals as well as signals obtained from human subjects under isotonic contraction.