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

3D muscle networks based on vibrational mechanomyography

Objective. Muscle network modeling maps synergistic control during complex motor tasks. Intermuscular coherence (IMC) is key to isolate synchronization underlying coupling in such neuromuscular control. Model inputs, however, rely on electromyography, which can limit the depth of muscle and spatial information acquisition across muscle fibers.Approach. We introduce three-dimensional (3D) muscle networks based on vibrational mechanomyography (vMMG) and IMC analysis to evaluate the functional co-modulation of muscles across frequency bands in concert with the longitudinal, lateral, and transverse directions of muscle fibers. vMMG is collected from twenty subjects using a bespoke armband of accelerometers while participants perform four hand gestures. IMC from four superficial muscles (flexor carpi radialis, brachioradialis, extensor digitorum communis, and flexor carpi ulnaris) is decomposed using matrix factorization into three frequency bands. We further evaluate the practical utility of the proposed technique by analyzing the network responses to various sensor-skin contact force levels, studying changes in quality, and discriminative power of vMMG.Main results. Results show distinct topological differences, with coherent coupling as high as 57% between specific muscle pairs, depending on the frequency band, gesture, and direction. No statistical decrease in signal strength was observed with higher contact force.Significance. Results support the usability vMMG as a tool for muscle connectivity analyses and demonstrate the use of IMC as a new feature space for hand gesture classification. Comparison of spectrotemporal and muscle network properties between levels of force support the robustness of vMMG-based network models to variations in tissue compression. We argue 3D models of vMMG-based muscle networks provide a new foundation for studying synergistic muscle activation, particularly in out-of-clinic scenarios where electrical recording is impractical.

Basic characteristics between mechanomyogram and muscle force during twitch and tetanic contractions in rat skeletal muscles

The mechanomyogram (MMG) is a signal measured by various vibration sensors for slight vibrations induced by muscle contraction, and it reflects the muscle force during electrically induced-contraction or until 60%-70% maximum voluntary contraction, so the MMG is considered an alternative and novel measurement tool for muscle strength. We simultaneously measured the MMG and muscle force in the gastrocnemius (GC), vastus intermedius (VI), and soleus (SOL) muscles of rats. The muscle force was measured by attaching a hook to the tendon using a load cell, and the MMG was measured using a charged-coupled device-type displacement sensor at the middle of the target muscle. The MMG-twitch waveform was very similar to that of the muscle force; however, the half relaxation time and relaxation time (10%), which are relaxation parameters, were prolonged compared to those of the muscle force. The MMG amplitude correlated with the muscle force. Since stimulation frequencies that are necessary to evoke tetanic progression have a significant correlation with the twitch parameter, there is a close relationship between twitch and tetanus in the MMG signal. Therefore, we suggest that the MMG, which is electrically induced and detected by a laser displacement sensor, may be an alternative tool for measuring muscle strength.

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.

Longitudinal, lateral and transverse axes of forearm muscles influence the crosstalk in the mechanomyographic signals during isometric wrist postures

Problem Statement

In mechanomyography (MMG), crosstalk refers to the contamination of the signal from the muscle of interest by the signal from another muscle or muscle group that is in close proximity.

Purpose

The aim of the present study was two-fold: i) to quantify the level of crosstalk in the mechanomyographic (MMG) signals from the longitudinal (L_o), lateral (L_a) and transverse (T_r) axes of the extensor digitorum (ED), extensor carpi ulnaris (ECU) and flexor carpi ulnaris (FCU) muscles during isometric wrist flexion (WF) and extension (WE), radial (RD) and ulnar (UD) deviations; and ii) to analyze whether the three-directional MMG signals influence the level of crosstalk between the muscle groups during these wrist postures.

Methods

Twenty, healthy right-handed men (mean ± SD: age = 26.7±3.83 y; height = 174.47±6.3 cm; mass = 72.79±14.36 kg) participated in this study. During each wrist posture, the MMG signals propagated through the axes of the muscles were detected using three separate tri-axial accelerometers. The x-axis, y-axis, and z-axis of the sensor were placed in the L_o, L_a, and T_r directions with respect to muscle fibers. The peak cross-correlations were used to quantify the proportion of crosstalk between the different muscle groups.

Results

The average level of crosstalk in the MMG signals generated by the muscle groups ranged from: 34.28–69.69% for the L_o axis, 27.32–52.55% for the L_a axis and 11.38–25.55% for the T_r axis for all participants and their wrist postures. The T_r axes between the muscle groups showed significantly smaller crosstalk values for all wrist postures [F (2, 38) = 14–63, p<0.05, η² = 0.416–0.769].

Significance

The results may be applied in the field of human movement research, especially for the examination of muscle mechanics during various types of the wrist postures.