Two-dimensional spatial distribution of surface mechanomyographical response to single motor unit activity

BiLSTM-Based Joint Torque Prediction From Mechanomyogram During Isometric Contractions: A Proof of Concept Study

Quantifying muscle strength is an important measure in clinical settings; however, there is a lack of practical tools that can be deployed for routine assessment. The purpose of this study is to propose a deep learning model for ankle plantar flexion torque prediction from time-series mechanomyogram (MMG) signals recorded during isometric contractions (i.e., a similar form to manual muscle testing procedure in clinical practice) and to evaluate its performance. Four different deep learning models in terms of model architecture (based on a stacked bidirectional long short-term memory and dense layers) were designed with different combinations of the number of units (from 32 to 512) and dropout ratio (from 0.0 to 0.8), and then evaluated for prediction performance by conducting the leave-one-subject-out cross-validation method from the 10-subject dataset. As a result, the models explained more variance in the untrained test dataset as the error metrics (e.g., root-mean-square error) decreased and as the slope of the relationship between the measured and predicted joint torques became closer to 1.0. Although the slope estimates appear to be sensitive to an individual dataset, >70% of the variance in nine out of 10 datasets was explained by the optimal model. These results demonstrated the feasibility of the proposed model as a potential tool to quantify average joint torque during a sustained isometric contraction.

Reliability of mechanomyographic amplitude measurements for trunk muscles during maximal voluntary isometric contraction

BACKGROUND

Mechanomyography (MMG) has been used to investigate mechanical characteristics of muscle contraction in clinical and experimental settings.

OBJECTIVE

The aim of this study was to determine the test-retest reliability of mechanomyographic amplitude (MMGRMS) measurements as a tool for measuring the maximal voluntary isometric contractions (MVICs) of trunk muscles in healthy participants.

METHODS

There were ten young adults participating in this study. Accelerometers were used to detect surface MMG signals from three trials of 5-s MVICs of the rectus abdominis, external obliques, erector spinae, and multifidus in the vertical, transverse, and longitudinal directions. Intraclass correlation coefficient (ICC), standard error of measurement (SEM), and minimum detectable change were calculated.

RESULTS

Good to excellent test-retest reliability of mechanomyographic amplitude (MMGRMS) measurements was achieved for all MVICs of trunk muscles in healthy participants, as indicated by ICCs ranging from 0.99 to 0.64 for MMGRMS of the trunk muscles during MVIC.

CONCLUSIONS

This study demonstrates that MMG is a reliable measurement to detect the activation amplitudes of trunk muscles during MVIC.

Muscle stiffness estimation using a system identification technique applied to evoked mechanomyogram during cycling exercise

The aims of this study were to develop a method to extract the evoked mechanomyogram (MMG) during cycling exercise and to clarify muscle stiffness at various cadences, workloads, and power. Ten young healthy male participants were instructed to pedal a cycle ergometer at cadences of 40 and 60 rpm. The loads were 4.9, 9.8, 14.7, and 19.6 N, respectively. One electrical stimulus per two pedal rotations was applied to the vastus lateralis muscle at a knee angle of 80° in the down phase. MMGs were measured using a capacitor microphone, and the MMGs were divided into stimulated and non-stimulated sequences. Each sequence was synchronously averaged. The synchronously averaged non-stimulated MMG was subtracted from the synchronously averaged stimulated MMG to extract an evoked MMG. The evoked MMG system was identified and the poles of the transfer function were calculated. The poles and mass of the vastus lateralis muscle were used to estimate muscle stiffness. Results showed that muscle stiffness was 186-626 N /m and proportional to the workloads and power. In conclusion, our method can be used to assess muscle stiffness proportional to the workload and power.

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.

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.

Propagation direction of natural mechanical oscillations in the biceps brachii muscle during voluntary contraction

The aim of the study was to determine the directionality of the coupling of mechanical vibrations across the biceps brachii muscle at different frequencies of interest during voluntary contraction. The vibrations that are naturally generated by skeletal muscles were recorded by a two-dimensional array of skin mounted accelerometers over the biceps brachii muscle (surface mechanomyogram, S-MMG) during voluntary isometric contractions in ten healthy young men. As a measure of the similarity of vibration between a given pair of accelerometers, the spatial coherence of S-MMG at low (f<25Hz) and high (f>25Hz) frequency bands were investigated to determine if the coupling of the natural mechanical vibrations were due to the different physiological muscle activity at low and high frequencies. In both frequency bands, spatial coherence values for sensor pairs aligned longitudinally along the proximal to distal ends of the biceps were significantly higher compared with those for the sensor pairs oriented perpendicular to the muscle fibers. This difference was more evident at the higher frequency band. The findings indicated that coherent mechanical oscillations mainly propagated along the longitudinal direction of the biceps brachii muscle fibers at high frequencies (f>25Hz).

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.

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

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

On functional motor adaptations: from the quantification of motor strategies to the prevention of musculoskeletal disorders in the neck-shoulder region

BACKGROUND

Occupations characterized by a static low load and by repetitive actions show a high prevalence of work-related musculoskeletal disorders (WMSD) in the neck-shoulder region. Moreover, muscle fatigue and discomfort are reported to play a relevant initiating role in WMSD.

AIMS

To investigate relationships between altered sensory information, i.e. localized muscle fatigue, discomfort and pain and their associations to changes in motor control patterns.

MATERIALS & METHODS

In total 101 subjects participated. Questionnaires, subjective assessments of perceived exertion and pain intensity as well as surface electromyography (SEMG), mechanomyography (MMG), force and kinematics recordings were performed.

RESULTS

Multi-channel SEMG and MMG revealed that the degree of heterogeneity of the trapezius muscle activation increased with fatigue. Further, the spatial organization of trapezius muscle activity changed in a dynamic manner during sustained contraction with acute experimental pain. A graduation of the motor changes in relation to the pain stage (acute, subchronic and chronic) and work experience were also found. The duration of the work task was shorter in presence of acute and chronic pain. Acute pain resulted in decreased activity of the painful muscle while in subchronic and chronic pain, a more static muscle activation was found. Posture and movement changed in the presence of neck-shoulder pain. Larger and smaller sizes of arm and trunk movement variability were respectively found in acute pain and subchronic/chronic pain. The size and structure of kinematics variability decreased also in the region of discomfort. Motor variability was higher in workers with high experience. Moreover, the pattern of activation of the upper trapezius muscle changed when receiving SEMG/MMG biofeedback during computer work.

DISCUSSION

SEMG and MMG changes underlie functional mechanisms for the maintenance of force during fatiguing contraction and acute pain that may lead to the widespread pain seen in WMSD. A lack of harmonious muscle recruitment/derecruitment may play a role in pain transition. Motor behavior changed in shoulder pain conditions underlining that motor variability may play a role in the WMSD development as corroborated by the changes in kinematics variability seen with discomfort. This prognostic hypothesis was further, supported by the increased motor variability among workers with high experience.

CONCLUSION

Quantitative assessments of the functional motor adaptations can be a way to benchmark the pain status and help to indentify signs indicating WMSD development. Motor variability is an important characteristic in ergonomic situations. Future studies will investigate the potential benefit of inducing motor variability in occupational settings.

The effects of accelerometer placement on mechanomyographic amplitude and mean power frequency during cycle ergometry

The purposes of this study were threefold: (1) to compare the power output related patterns of absolute and normalized MMG amplitude and MPF responses for proximal and distal accelerometer placements on the vastus lateralis (VL) muscle during incremental cycle ergometry; (2) to examine the influence of accelerometer placements on mean absolute MMG amplitude and MPF values; and (3) to determine the effects of normalization on mean MMG amplitude and MPF values from proximal and distal accelerometer placements. Fifteen adults (10 men and 5 women; mean+/-SD age=23.9+/-3.1 years) performed incremental cycle ergometry tests to exhaustion. Two accelerometers were placed proximal and distal on the VL muscle. Paired t-tests indicated that absolute MMG amplitude values for the proximal accelerometer were greater (p<0.05) than the distal accelerometer at all power outputs. The normalized MMG amplitude also had greater values for the proximal accelerometer at all power outputs, except 50W. There were no differences, however, between proximal and distal accelerometers for absolute MMG MPF, except at 75W, and normalization eliminated this difference. Twenty-seven percent of the subjects exhibited different power output related patterns of responses between accelerometer placements for MMG amplitude and 47% exhibited different patterns for MPF. These findings indicated that normalization did not eliminate the influence of accelerometer placement on MMG amplitude and highlighted the importance of standardizing accelerometer placements to compare MMG values during cycle ergometry.

Cross-correlation analysis of mechanomyographic signals detected in two axes

The purpose of this study was to use laser displacement sensors to examine the cross-correlation of surface mechanomyographic (MMG) signals detected from the rectus femoris muscle in perpendicular and transverse axes during isometric muscle actions of the leg extensors. Ten healthy men (mean +/- SD age = 22.1 +/- 1.6 years) and ten healthy women (age = 24.4 +/- 2.8 years) volunteered to perform submaximal to maximal isometric muscle actions of the dominant leg extensors. During each muscle action, two separate MMG signals were detected from the rectus femoris with laser displacement sensors. One MMG sensor was oriented in an axis that was perpendicular (PERP) to the muscle surface, and the second sensor was oriented in an axis that was transverse (TRAN) to the muscle surface. For each subject and force level, the MMG signals from the PERP and TRAN sensors were cross-correlated. The results showed maximum cross-correlation coefficients that ranged from R(x)(,y) = 0.273 to 0.989, but all subjects demonstrated at least one coefficient greater than 0.89. These findings showed a high level of association between the MMG signals detected in the perpendicular and transverse axes. Thus, it may not be necessary to detect MMG signals in multiple axes.

Simple and cost-effective hardware and software for functional brain mapping using intrinsic optical signal imaging

We describe a simple and low-cost system for intrinsic optical signal (IOS) imaging using stable LED light sources, basic microscopes, and commonly available CCD cameras. IOS imaging measures activity-dependent changes in the light reflectance of brain tissue, and can be performed with a minimum of specialized equipment. Our system uses LED ring lights that can be mounted on standard microscope objectives or video lenses to provide a homogeneous and stable light source, with less than 0.003% fluctuation across images averaged from 40 trials. We describe the equipment and surgical techniques necessary for both acute and chronic mouse preparations, and provide software that can create maps of sensory representations from images captured by inexpensive 8-bit cameras or by 12-bit cameras. The IOS imaging system can be adapted to commercial upright microscopes or custom macroscopes, eliminating the need for dedicated equipment or complex optical paths. This method can be combined with parallel high resolution imaging techniques such as two-photon microscopy.

Heterogeneous mechanomyographic absolute activation of paraspinal muscles assessed by a two-dimensional array during short and sustained contractions

Spatial dependency of paraspinal muscle activity was assessed using a new two-dimensional MMG recording system. MMG signals were detected over the left and right paraspinal muscles of 10 volunteers using a grid of 12 accelerometers. During two separate trials subjects maintained a 20 degrees flexed position and held loads that ranged from 0 to 15 kg (in 2.5 kg increments) for 20s; and 7.5 kg for 6 min. Maps of absolute and normalised (with respect to initial values) average rectified value, mean power frequency, variance and skewness of the power spectral density were obtained from the two-dimensional MMG recordings. For both the short duration and sustained contractions, the MMG absolute average rectified value, mean power frequency, variance and skewness depended on accelerometer location (P<0.05), while, with the exception of the skewness (P<0.05), normalised values did not. These results demonstrate both inhomogeneous MMG absolute activity and homogeneous MMG normalised activity in paraspinal muscles for short duration and sustained contractions. Moreover, the effect of accelerometer location on spectral variables confirmed the limited validity of general relationships between MMG spectral changes and motor unit recruitment strategies. This study underlines the importance of using multiple recording sites when assessing back muscle activity.

Spatial and force dependency of mechanomyographic signal features

The aim was to investigate with a novel technique the spatial inhomogeneity in surface mechanomyographic (MMG) response to muscle contraction at varying force levels. MMG signals were detected over the dominant tibialis anterior muscle of 10 volunteers using a 5 x 3 grid of accelerometers. The subjects performed 3 s long isometric contractions at forces ranging from 0% to 100% of the maximal force (10% increment) in a randomised order. From the two-dimensional MMG recordings, maps of absolute and normalized temporal and spectral MMG descriptors were obtained. The centroid and entropy of these maps were computed to describe the spatial centre of activity and degree of homogeneity, respectively. MMG absolute amplitude did not depend on location over the muscle while normalized amplitude did and the centroid shifted with increasing force. Amplitude increased with force and its entropy decreased. Absolute and normalized spectral variables depended on location over the muscle and their centroid shifted with increasing force. In addition, the dependency of absolute and normalized spectral variables on force was affected by location. These results highlight limitations when using single-channel MMG features for the assessment of motor unit control strategies, due to a substantial effect of position on the relation between force and MMG characteristics.