Technical aspects of mechnomyography recording with piezoelectric contact sensor

Targeting Transcutaneous Spinal Cord Stimulation Using a Supervised Machine Learning Approach Based on Mechanomyography

Transcutaneous spinal cord stimulation (tSCS) provides a promising therapy option for individuals with injured spinal cords and multiple sclerosis patients with spasticity and gait deficits. Before the therapy, the examiner determines a suitable electrode position and stimulation current for a controlled application. For that, amplitude characteristics of posterior root muscle (PRM) responses in the electromyography (EMG) of the legs to double pulses are examined. This laborious procedure holds potential for simplification due to time-consuming skin preparation, sensor placement, and required expert knowledge. Here, we investigate mechanomyography (MMG) that employs accelerometers instead of EMGs to assess muscle activity. A supervised machine-learning classification approach was implemented to classify the acceleration data into no activity and muscular/reflex responses, considering the EMG responses as ground truth. The acceleration-based calibration procedure achieved a mean accuracy of up to 87% relative to the classical EMG approach as ground truth on a combined cohort of 11 healthy subjects and 11 patients. Based on this classification, the identified current amplitude for the tSCS therapy was in 85%, comparable to the EMG-based ground truth. In healthy subjects, where both therapy current and position have been identified, 91% of the outcome matched well with the EMG approach. We conclude that MMG has the potential to make the tuning of tSCS feasible in clinical practice and even in home use.

A scoping review of current and emerging techniques for evaluation of peripheral nerve health, degeneration, and regeneration: part 1, neurophysiology

Peripheral neuroregeneration research and therapeutic options are expanding exponentially. With this expansion comes an increasing need to reliably evaluate and quantify nerve health. Valid and responsive measures that can serve as biomarkers of the nerve status are essential for both clinical and research purposes for diagnosis, longitudinal follow-up, and monitoring the impact of any intervention. Furthermore, such biomarkers can elucidate regeneration mechanisms and open new avenues for research. Without these measures, clinical decision-making falls short, and research becomes more costly, time-consuming, and sometimes infeasible. As a companion to Part 2, which is focused on non-invasive imaging, Part 1 of this two-part scoping review systematically identifies and critically examines many current and emerging neurophysiological techniques that have the potential to evaluate peripheral nerve health, particularly from the perspective of regenerative therapies and research.

Physiological factors affecting the mechanical performance of peripheral muscles: A perspective for long COVID patients through a systematic literature review

Background: Peripheral muscle weakness can be measured quantitatively in long COVID patients. Mechanomyography (MMG) is an alternative tool to measure muscle strength non-invasively.

Objective: This literature review aims to provide evidence on the efficacy of MMG in measuring muscle strength for long COVID patients and to determine the physiological factors that may affect the use of MMG in assessing muscle performance.

Methods: A systematic literature review was conducted using EBSCO’s MEDLINE Complete. A total of five out of 2,249 potential publications fulfilled the inclusion criteria.

Results: The selected studies addressed muscle performance based on the physiological effects of age, gender, and physical activity level. MMG is sensitive in measuring muscle strength for long COVID patients due to its higher signal-to-noise ratio and lightweight accelerometers. Its neglectable skin impedance and low risk of influences during the recording of surface motions make MMG a reliable tool.

Conclusion: Muscle performance is affected by age, gender, and physical activity level. Sensors, such as MMG, as well as the length of the muscle and the characteristics of the muscle activity, are important considerations when choosing a sensor for diagnostic evaluation. The efficacy of MMG in measuring muscle strength for long COVID patients and the physiological factors that may affect the use of MMG in assessing muscle performance are discussed.

A Coupled Piezoelectric Sensor for MMG-Based Human-Machine Interfaces

Mechanomyography (MMG) is a technique of recording muscles activity that may be considered a suitable choice for human–machine interfaces (HMI). The design of sensors used for MMG and their spatial distribution are among the deciding factors behind their successful implementation to HMI. We present a new design of a MMG sensor, which consists of two coupled piezoelectric discs in a single housing. The sensor’s functionality was verified in two experimental setups related to typical MMG applications: an estimation of the force/MMG relationship under static conditions and a neural network-based gesture classification. The results showed exponential relationships between acquired MMG and exerted force (for up to 60% of the maximal voluntary contraction) alongside good classification accuracy (94.3%) of eight hand motions based on MMG from a single-site acquisition at the forearm. The simplification of the MMG-based HMI interface in terms of spatial arrangement is rendered possible with the designed sensor.

Wearable MMG-Plus-One Armband: Evaluation of Normal Force on Mechanomyography (MMG) to Enhance Human-Machine Interfacing

In this paper, we introduce a new mode of mechanomyography (MMG) signal capture for enhancing the performance of human-machine interfaces (HMIs) through modulation of normal pressure at the sensor location. Utilizing this novel approach, increased MMG signal resolution is enabled by a tunable degree of freedom normal to the sensor-skin contact area. We detail the mechatronic design, experimental validation, and user study of an armband with embedded acoustic sensors demonstrating this capacity. The design is motivated by the nonlinear viscoelasticity of the tissue, which increases with the normal surface pressure. This, in theory, results in higher conductivity of mechanical waves and hypothetically allows to interface with deeper muscle; thus, enhancing the discriminative information context of the signal space. Ten subjects (seven able-bodied and three trans-radial amputees) participated in a study consisting of the classification of hand gestures through MMG while increasing levels of contact force were administered. Four MMG channels were positioned around the forearm and placed over the flexor carpi radialis, brachioradialis, extensor digitorum communis, and flexor carpi ulnaris muscles. A total of 852 spectrotemporal features were extracted (213 features per each channel) and passed through a Neighborhood Component Analysis (NCA) technique to select the most informative neurophysiological subspace of the features for classification. A linear support vector machine (SVM) then classified the intended motion of the user. The results indicate that increasing the normal force level between the MMG sensor and the skin can improve the discriminative power of the classifier, and the corresponding pattern can be user-specific. These results have significant implications enabling embedding MMG sensors in sockets for prosthetic limb control and HMI.

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.

Verification of nerve decompression using mechanomyography

BACKGROUND CONTEXT

Assessment of nerve root decompression in surgery is largely based on visualization and tactile feedback. Often times, visualization can be limited, such as in minimally invasive surgery, and tactile feedback is a subjective assessment that makes the evaluation of successful nerve decompression difficult. Electromyography (EMG) has been proposed as an assessment tool, but EMG responses are often difficult to quantify. Alternatively, mechanomyography (MMG) provides a quantifiable response with high signal-to-noise ratio compared with EMG. MMG provides a sensitive tool to accurately quantify mechanical responses to motor action potentials generated by electrical stimulus, allowing more reliable assessment of nerve decompression.

PURPOSE

The aim of this study was to assess the ability of MMG to quantitatively demonstrate successful nerve root decompression.

STUDY DESIGN

Prospective cohort, Therapeutic Level III, Urban Level I Trauma Center.

PATIENT SAMPLE

A total of 46 patients (72 affected nerve roots) undergoing decompression procedures for lower extremity radiculopathy caused by nerve root compression were enrolled in the study. The study population included 15 patients with herniated nucleus pulposus (HNP) and 31 with lateral recess stenosis (LRS).

OUTCOME MEASURE

Visual analog scale (VAS) score.

METHODS

A total of 72 nerves roots in 46 patients undergoing lumbar decompression procedures, for lower extremity radicular symptoms, were tested using MMG. Nerves were stimulated upstream from the compression site, and the lowest threshold current needed to generate a muscle response was determined. Signal response sizes were recorded before and after decompression. VAS scores were collected pre- and postoperatively.

RESULTS

Of the patients, 90% (65/72) had elevated stimulation thresholds (>1 milliamp [mA]) before decompression. After decompression, 98% of patients (64/65) with elevated current thresholds exhibited a drop in threshold of ≥1 mA (p<.001). A postdecompression increase in response amplitude was recorded in all patients. VAS scores improved postdecompression (6.8 vs. 1.1, p<.001) with a positive correlation between decreased stimulation thresholds and degree of improvement in VAS scores (p<.001).

CONCLUSION

MMG is an effective tool that can be used to differentiate normal and compressed nerves by quantifying the mechanomyographic response to a stimulating current. MMG allows one to measure the effect of decompression, judge its effectiveness in real time, and eliminate the subjectivity seen in tactile feedback methods. When the adequacy of decompression is uncertain, MMG can guide the surgeon toward additional or alternative procedures to ensure complete nerve root decompression.

Mechanomyography and muscle function assessment: a review of current state and prospects

Previous studies have explored to saturation the efficacy of the conventional signal (such as electromyogram) for muscle function assessment and found its clinical impact limited. Increasing demand for reliable muscle function assessment modalities continues to prompt further investigation into other complementary alternatives. Application of mechanomyographic signal to quantify muscle performance has been proposed due to its inherent mechanical nature and ability to assess muscle function non-invasively while preserving muscular neurophysiologic information. Mechanomyogram is gaining accelerated applications in evaluating the properties of muscle under voluntary and evoked muscle contraction with prospects in clinical practices. As a complementary modality and the mechanical counterpart to electromyogram; mechanomyogram has gained significant acceptance in analysis of isometric and dynamic muscle actions. Substantial studies have also documented the effectiveness of mechanomyographic signal to assess muscle performance but none involved comprehensive appraisal of the state of the art applications with highlights on the future prospect and potential integration into the clinical practices. Motivated by the dearth of such critical review, we assessed the literature to investigate its principle of acquisition, current applications, challenges and future directions. Based on our findings, the importance of rigorous scientific and clinical validation of the signal is highlighted. It is also evident that as a robust complement to electromyogram, mechanomyographic signal may possess unprecedented potentials and further investigation will be enlightening.

Comparison of displacement and acceleration transducers for the characterization of mechanics of muscle and subcutaneous tissues by system identification of a mechanomyogram

The purpose of this study was to clarify the performance of transducers for the mechanical characterization of muscle and subcutaneous tissue with the aid of a system identification technique. The common peroneal nerve was stimulated, and a mechanomyogram (MMG) of the anterior tibialis muscle was detected with a laser displacement meter or an acceleration sensor. The transfer function between stimulation and the MMG was identified by the singular value decomposition method. The MMG detected with a laser displacement meter, DMMG, was approximated with a second-order model, but that detected with an acceleration sensor, AMMG, was approximated with a sixth-order model. The natural frequency of the DMMG coincided with that in the literature and was close to the lowest natural frequency of the AMMG. The highest natural frequency of the AMMG was within the range of the resonance frequencies of human soft tissue. The laser displacement meter is suitable for the precise identification of the MMG, which has a natural frequency of around 3 Hz. The acceleration transducer is suitable for the identification of the MMG with natural frequencies of tens of hertz.

Mechanomyographic activity in the human lateral pterygoid muscle during mandibular movement

The activity of the lateral pterygoid muscle has been regarded to be related to the pathological condition of the temporomandibular joint (TMJ) in the craniomandibular disorders. Because the lateral pterygoid muscle is a deep muscle, a needle electrode is necessary for EMG recordings. The purpose of this study was to establish a non-invasive method for the evaluation of muscle activity of the lateral pterygoid muscle using mechanomyogram (MMG). In three male subjects, surface electromyogram (EMG) in the left masseter muscle, left anterior and posterior belly of the temporal muscle, left anterior belly of the digastric muscle and needle EMG of the inferior head of the lateral pterygoid were recorded during mandibular movement tasks simultaneously with the MMG derived from a condenser microphone in the external ear canal. There were significant positive correlations between the needle EMG signal of the lateral pterygoid muscle and the MMG signal for the tasks of static jaw opened position of 30 mm of interincisal distance (p=0.000, R(2)=0.725), static jaw opened position of 40 mm of interincisal distance (p=0.000, R(2)=0.753), 5mm protruded mandibular position (p=0.000, R(2)=0.653), the most protruded mandibular position (p=0.000, R(2)=0803). On the contrary, for the task of maximal clenching, there was no significant correlation between the EMG signal of the lateral pterygoid muscle and the MMG signal. These results suggest that the activity of the lateral pterygoid muscle could be evaluated by the MMG signals recorded in the external ear canal, unless jaw closing major muscles show active contraction.

A review of non-invasive techniques to detect and predict localised muscle fatigue

Muscle fatigue is an established area of research and various types of muscle fatigue have been investigated in order to fully understand the condition. This paper gives an overview of the various non-invasive techniques available for use in automated fatigue detection, such as mechanomyography, electromyography, near-infrared spectroscopy and ultrasound for both isometric and non-isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who wish to select the most appropriate methodology for research on muscle fatigue detection or prediction, or for the development of devices that can be used in, e.g., sports scenarios to improve performance or prevent injury. To date, research on localised muscle fatigue focuses mainly on the clinical side. There is very little research carried out on the implementation of detecting/predicting fatigue using an autonomous system, although recent research on automating the process of localised muscle fatigue detection/prediction shows promising results.

The effect of accelerometer location on the classification of single-site forearm mechanomyograms

Background

Recently, pattern recognition methods have been deployed in the classification of multiple activation states from mechanomyogram (MMG) signals for the purpose of controlling switching interfaces. Given the propagative properties of MMG signals, it has been suggested that MMG classification should be robust to changes in sensor placement. Nonetheless, this purported robustness remains speculative to date. This study sought to quantify the change in classification accuracy, if any, when a classifier trained with MMG signals from the muscle belly, is subsequently tested with MMG signals from a nearby location.

Methods

An arrangement of 5 accelerometers was attached to the flexor carpi radialis muscle of 12 able-bodied participants; a reference accelerometer was located over the muscle belly, two peripheral accelerometers were positioned along the muscle's transverse axis and two more were aligned to the muscle's longitudinal axis. Participants performed three classes of muscle activity: wrist flexion, wrist extension and semi-pronation. A collection of time, frequency and time-frequency features were considered and reduced by genetic feature selection. The classifier, trained using features from the reference accelerometer, was tested with signals from the longitudinally and transversally displaced accelerometers.

Results

Classification degradation due to accelerometer displacement was significant for all participants, and showed no consistent trend with the direction of displacement. Further, the displaced accelerometer signals showed task-dependent de-correlations with respect to the reference accelerometer.

Conclusions

These results indicate that MMG signal features vary with spatial location and that accelerometer displacements of only 1-2 cm cause sufficient feature drift to significantly diminish classification accuracy. This finding emphasizes the importance of consistent sensor placement between MMG classifier training and deployment for accurate control of switching interfaces.

The design and testing of a novel mechanomyogram-driven switch controlled by small eyebrow movements

Background

Individuals with severe physical disabilities and minimal motor behaviour may be unable to use conventional mechanical switches for access. These persons may benefit from access technologies that harness the volitional activity of muscles. In this study, we describe the design and demonstrate the performance of a binary switch controlled by mechanomyogram (MMG) signals recorded from the frontalis muscle during eyebrow movements.

Methods

Muscle contractions, detected in real-time with a continuous wavelet transform algorithm, were used to control a binary switch for computer access. The automatic selection of scale-specific thresholds reduced the effect of artefact, such as eye blinks and head movement, on the performance of the switch. Switch performance was estimated by cued response-tests performed by eleven participants (one with severe physical disabilities).

Results

The average sensitivity and specificity of the switch was 99.7 ± 0.4% and 99.9 ± 0.1%, respectively. The algorithm performance was robust against typical participant movement.

Conclusions

The results suggest that the frontalis muscle is a suitable site for controlling the MMG-driven switch. The high accuracies combined with the minimal requisite effort and training show that MMG is a promising binary control signal. Further investigation of the potential benefits of MMG-control for the target population is warranted.

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.

Use of MMG signals for the control of powered orthotic devices: development of a rectus femoris measurement protocol

A test protocol is defined for the purpose of measuring rectus femoris mechanomyographic (MMG) signals. The protocol is specified in terms of the following: measurement equipment, signal processing requirements, human postural requirements, test rig, sensor placement, sensor dermal fixation, and test procedure. Preliminary tests of the statistical nature of rectus femoris MMG signals were performed, and Gaussianity was evaluated by means of a two-sided Kolmogorov-Smirnov test. For all 100 MMG data sets obtained from the testing of two volunteers, the null hypothesis of Gaussianity was rejected at the 1%, 5%, and 10% significance levels. Most skewness values were found to be greater than 0.0, while all kurtosis values were found to be greater than 3.0. A statistical convergence analysis also performed on the same 100 MMG data sets suggested that 25 MMG acquisitions should prove sufficient to statistically characterize rectus femoris MMG. This conclusion is supported by the qualitative characteristics of the mean rectus femoris MMG power spectral densities obtained using 25 averages.

Laser-detected lateral muscle displacement is correlated with force fluctuations during voluntary contractions in humans

Fluctuations in muscle force during steady voluntary contractions result from the summation of twitch forces produced by asynchronous activation of multiple motor units. We hypothesized that oscillatory lateral muscle displacement, measured with a non-contact high-resolution laser displacement sensor, is correlated with force fluctuations during steady, voluntary contractions with a human muscle. Eight healthy young adults (20-33 yrs) performed steady isometric contractions with the first dorsal interosseus muscle. Contraction intensity ranged from 2.5% to 60% of the maximal voluntary contraction force. Oscillatory lateral displacement of the muscle surface was measured with a high-resolution laser displacement sensor (0.5 microm resolution), concurrently with abduction force of the index finger. In the time-domain analysis, there was a significant positive peak in the cross-correlation function between lateral muscle displacement and force fluctuations. In addition, the amplitude increased linearly with contraction intensity in both signals. In the frequency-domain analysis, frequency content was similar in both signals, and there was significant coherence between signals for the major frequency range of the signals (<5 Hz). In conclusion, laser-detected lateral displacement of a hand muscle is correlated with force fluctuations across a wide range of contraction intensity during steady voluntary contractions in humans.

Assessment of respiratory muscle effort studying diaphragm movement registered with surface sensors. Animal model (dogs)

The diaphragm movement (DM) signal during spontaneous ventilations is analyzed in this work. The DM signal is acquired by means two surface sensors (a piezoelectric contact sensor -PCS- and a piezoelectric accelerometer -ACP) applied on the costal wall. The main objective is to develop a new non invasive technique to assess respiratory muscle effort. Experiments were performed in an animal model: four pentobarbital-anesthetized and two awake mongrel dogs, carrying out spontaneous ventilations against an inspiratory load. DM signal has been decomposed in two components: a low frequency component (lower than 5 Hz) due to the overall lateral movement of the muscle (MOV component), and a high frequency component (higher than 5 Hz) due to the lateral vibration of active muscle fibers (VIB component). It has been seen that the PCS acquires only MOV components of MD signal, while ACP acquires both components. Positive high correlation coefficients have been found between amplitude parameters of VIB components of DM signal, acquired by means the ACP, and the respiratory muscle effort during ventilation, measured with inspiratory pressures.

Acute effects of static stretching on characteristics of the isokinetic angle - torque relationship, surface electromyography, and mechanomyography

The aims of this study were to examine the acute effects of static stretching on peak torque, work, the joint angle at peak torque, acceleration time, isokinetic range of motion, mechanomyographic amplitude, and electromyographic amplitude of the rectus femoris during maximal concentric isokinetic leg extensions at 1.04 and 5.23 rad x s(-1) in men and women. Ten women (mean +/- s: age 23.0 +/- 2.9 years, stature 1.61 +/- 0.12 m, mass 63.3 +/- 9.9 kg) and eight men (age 21.4 +/- 3.0 years, stature 1.83 +/- 0.11 m, mass 83.1 +/- 15.2 kg) performed maximal voluntary concentric isokinetic leg extensions at 1.04 and 5.23 rad x s(-1). Following the initial isokinetic tests, the dominant leg extensors were stretched using four static stretching exercises. After the stretching, the isokinetic tests were repeated. Peak torque, acceleration time, and electromyographic amplitude decreased (P< or = 0.05) from pre- to post-stretching at 1.04 and 5.23 rad . s(-1); there were no changes (P > 0.05) in work, joint angle at peak torque, isokinetic range of motion, or mechanomyographic amplitude. These findings indicate no stretching-related changes in the area under the angle - torque curve (work), but a significant decrease in peak torque, which suggests that static stretching may cause a "flattening" of the angle - torque curve that reduces peak strength but allows for greater force production at other joint angles. These findings, in conjunction with the increased limb acceleration rates (decreased acceleration time) observed in the present study, provide tentative support for the hypothesis that static stretching alters the angle - torque relationship and/or sarcomere shortening velocity.

A comparison between mechanomyographic condenser microphone and accelerometer measurements during submaximal isometric, concentric and eccentric contractions

The aim of this study was to compare mechanomyogram (MMG) recorded by a condenser microphone (MIC) and an accelerometer (ACC) during submaximal isometric, concentric and eccentric contractions in 14 males. The maximal voluntary force (MVC) of the biceps brachii was measured. The subjects were asked to do short duration isometric, concentric and eccentric contraction at 10%, 30%, 50%, 70% MVC twice. For the concentric and eccentric contraction, the subject bent his arm for 3s (concentric) then held it for 3s and extended (eccentric) during 3s. The normalized root mean square (RMS) and mean power frequency (MPF) increased linearly with increased force for both transducers. There was a correlation between MIC MPF and ACC MPF at 10%, 30%, 50% MVC, and between MIC RMS and ACC RMS at 30% MVC during isometric contractions. There was significantly higher MPF for the ACC than for the MIC in concentric and eccentric modes, while the RMS did not differ among transducers in the three contraction modes. The RMS and MPF values coefficient of variations were significantly larger during anisometric contractions compared with isometric contractions and were lower for the accelerometer than for the microphone. The present results obtained during isometric, concentric and eccentric contractions of increased intensity showed that the information contained in microphone- and accelerometer-based MMG signals is different despite similar trends. It can be concluded that at low-moderate movement velocity, concentric contractions can be investigated by means of accelerometer and microphone.

A wavelet multiscale based method to separate the high and low frequency components of mechanomyographic signals

The study of mechanomyographic (MMG) signals during dynamic contraction requires a criterion to select the cut-off frequency of the filter utilized to separate the low frequency (L.F) component (basically due to gross movement of the muscle or of the body) and the high frequency (HF) component (related with the vibration of the muscle fibers during contraction). To date, there is not an established criterion to carry out this selection. In this study, we propose a wavelet multiscale based method to aid to select a suitable cut-off frequency to separate correctly the LF and HF components. This method has been tested in an animal model, with the signal acquired during spontaneous ventilations with a capacitive accelerometer applied on the costal wall. This signal, as the MMG signals during dynamic contractions, has a LF component that is related with the movement of the thoracic cage, and a HF component that could be related with the vibration of diaphragm muscle fibers during contraction. The results obtained in the two respiratory tests analyzed indicate that cut-off frequencies around 10 and 3 Hz, respectively, must be employed to eliminate the LF component. The proposed wavelet multiscale method appears to be suitable to carry out a preliminary study of the MMG frequency content in dynamic contraction protocols.

Comparison of a piezoelectric contact sensor and an accelerometer for examining mechanomyographic amplitude and mean power frequency versus torque relationships during isokinetic and isometric muscle actions of the biceps brachii

The purpose of this study was to compare a piezoelectric contact sensor with an accelerometer for measuring the mechanomyographic (MMG) signal from the biceps brachii during submaximal to maximal isokinetic and isometric forearm flexion muscle actions. Following determination of isokinetic peak torque (PT) and the isometric maximum voluntary contraction (MVC), 10 adults (mean+/-SD age=22.8+/-2.7yrs) performed randomly ordered, submaximal step muscle actions of the dominant forearm flexors in 20% increments from 20% to 80% PT and MVC. Surface MMG signals were recorded simultaneously from a contact sensor and an accelerometer placed over the belly of the biceps brachii muscle. During the isokinetic and isometric muscle actions, the contact sensor and accelerometer resulted in linear increases in normalized MMG amplitude with torque (r(2) range=0.84-0.97) but the linear slope of the normalized MMG amplitude versus isokinetic torque relationship for the accelerometer was less (p<0.10) than that of the contact sensor. There was no significant (p>0.05) relationship for normalized MMG mean power frequency (MPF, %max) versus isokinetic and isometric torque for the contact sensor, but the accelerometer demonstrated a quadratic (R(2)=0.94) or linear (r(2)=0.83) relationship for the isokinetic and isometric muscle actions, respectively. There were also a number of significant (p<0.05) mean differences between the contact sensor and accelerometer for normalized MMG amplitude or MPF values. These findings indicated that in some cases involving dynamic and isometric muscle actions, the contact sensor and accelerometer resulted in different torque-related responses that may affect the interpretation of the motor control strategies involved.

Surface electromyography and mechanomyography recording: a new differential composite probe

The objective of the study was to develop a new surface probe for differential mechanomyographic (MMG) and electromyographic (EMG) recording. Differential amplification is commonly used in electromyography to improve the signal-to-noise ratio. A new composite probe was developed with two electrodes (EMG) and two identical piezo-electric membranes (MMG) to be positioned on muscle. The probe had two built-in fixed-gain differential amplifiers: one to amplify the electric signal and the other to amplify the vibration signal. A similar non-differential MMG probe was used for comparisons. Burst muscular activity was recorded using the non-differential and differential probes and was used to test the performance of the two probes in suppressing artifacts of non-muscular origin. Power spectrum analysis of signals from the two probes showed that differential amplification significantly improved the signal-to-noise ratio in MMG recordings and significantly suppressed artifacts (power difference > 90%). The composite probe allowed simultaneous differential recording of MMG and EMG signals from the same muscular site. It recorded muscular activity more efficiently than the non-differential probe and could therefore be useful in studying fatigue and neuromuscular diseases.

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.

Mechanomyography versus electromyography, in monitoring the muscular fatigue

BACKGROUND

The use of the mechanomyogram (MMG) which detects muscular vibrations generated by fused individual fiber twitches has been refined. The study addresses a comparison of the MMG and surface electromyogram (SEMG) in monitoring muscle fatigue.

METHODS

The SEMG and MMG were recorded simultaneously from the same territory of motor units in two muscles (Biceps, Brachioradialis) of the human (n = 18), during sustained contraction at 25 % MVC (maximal voluntary contraction).

RESULTS

The RMS (root mean square) of the SEMG and MMG increased with advancing fatigue; MF (median frequency) of the PSD (power density spectra) progressively decreased from the onset of the contraction. These findings (both muscles, all subjects), demonstrate both through the SEMG and MMG a central component of the fatigue. The MF regression slopes of MMG were closer to each other between men and women (Biceps 1.55%; Brachialis 13.2%) than were the SEMG MF slopes (Biceps 25.32%; Brachialis 17.72%), which shows a smaller inter-sex variability for the MMG vs. SEMG.

CONCLUSION

The study presents another quantitative comparison (MF, RMS) of MMG and SEMG, showing that MMG signal can be used for indication of the degree of muscle activation and for monitoring the muscle fatigue when the application of SEMG is not feasible (chronical implants, adverse environments contaminated by electrical noise).

Surface myomechanical responses recorded on a scanner galvanometer

A moving magnet galvanometer equipped with lever and indentor was evaluated for mechanomyography (MMG). First, the precision of the galvanometer was tested on a piezo-electric disc actuator. Using a 50 mm lever, synthesised micromotions with an amplitude of 1 microm could be detected (noise level < 0.2 microm) at static indentation forces ranging from 0.1 to 2 N. Then the galvanometer was mounted on an isometric ankle dynamometer to sense calf-muscle responses (N = 6). In the first protocol, twitch contractions were elicited by electrical stimulation while the indentation force was increased. Twitch amplitudes, twitch contraction times and twitch half-relaxation times were analysed from the surface and contraction responses. With indentation force (0.1-0.5 N), the amplitude of the surface responses increased (+61%), contraction and half-relaxation times, however, were not influenced. The mean twitch contraction time from the surface responses (60 +/- 11 ms) was shorter than that from the contraction responses (115 +/- 7 ms), indicating more fast-contracting fibres under the indented area. In the second protocol, voluntary target contractions were produced, and the surface responses were simultaneously recorded on an accelerometer. After double differentiation of the galvanometer signal, both acceleration MMGs showed a high coincidence in the time and frequency domains. With an indentation force of 2 N applied on the accelerometer, the signal amplitude (-10%) and the mean frequency (-19%) decreased. A specific application of this galvanometer-dynamometer test system is the assessment of regeneration processes in paraplegics with long-term denervated muscles.

Mechanical behaviour of condenser microphone in mechanomyography

Condenser microphones (MIC) have been widely used in mechanomyography, together with accelerometers and piezoelectric contact sensors. The aim of the present investigation was to clarify the mechanical variable (acceleration, velocity or displacement) indicated by the signal from a MIC transducer using a mechanical sinusoidal vibration system. In addition, the mechanomyogram (MMG) was recorded simultaneously with a MIC transducer and accelerometer (ACC) during voluntary contractions to confirm the mechanical variable reflected by the actual MMG and to examine the influence of motion artifact on the MMG. To measure the displacement-frequency response, mechanical sinusoidal vibrations of 3 to 300 Hz were applied to the MIC transducer with different sizes of air chambers (5, 10, 15 and 20 mm in diameter and 15, 20 or 25 mm long). The MIC transducer showed a linear relationship between the output amplitude and the vibration displacement, however, its frequency response declined with decreasing diameter and decreasing length of the air chamber. In fact, the cut-off frequency (-3dB) of the MIC transducer with the 5-mm-diameter chamber was 10, 8 and 4 Hz for the length 15, 20 and 25 mm, respectively. The air chamber with at least a diameter of 10 mm and a length of 15 mm is recommended for the MIC transducer. The sensitivity of this MIC transducer arrangement was 92 mV microm(-1) when excited at 100 Hz. During voluntary contraction, the amplitude spectral density function of the MMG from the MIC transducer resembled that of the double integral of the ACC transducer signal. The angle of the MIC transducer was delayed by 180 degrees in relation to the ACC transducer signal. The sensitivity of the MIC transducer was reduced to one-third because of the peculiar volume change of air chamber when the MMG was detected on the surface of the skin. In addition, the MIC transducer was contaminated by a smaller motion artifact than that from the ACC transducer. The maximal peak amplitude of the MIC and ACC transducer signal with the motion artifact was 7.7 and 12.3 times as much as the RMS amplitude of each signal without the motion artifact, respectively. These findings suggest that the MIC transducer acts as a displacement meter in the MMG. The MIC transducer seems to be a possible candidate for recording the MMG during dynamic muscle contractions as well as during sustained contractions.