Non-invasive assessment of single motor unit mechanomyographic response and twitch force by spike-triggered averaging.
ABSTRACT A method for non-invasive assessment of single motor unit (MU) properties from electromyographic (EMG), mechanomyographic (MMG) and force signals is proposed. The method is based on the detection and classification of single MU action potentials from interference multichannel surface EMG signals and on the spike-triggered average of the MMG (detected by an accelerometer) and force signals. The first dorsal interosseous (FDI) and abductor digiti minimi (ADM) muscles were investigated at contraction levels of 2% and 5% of the maximum voluntary contraction (MVC) force. A third contraction was performed by selective activation of a single MU with surface MU action potential visual feedback provided to the subject. At 5% MVC, the mean (+/-standard error) single MU MMG peak-to-peak value was 11.0+/-1.8 mm s(-2) (N= 17) and 32.3+/-6.5 mm s(-2) (N=20) for the FDI and ADM muscles, respectively. The peak of the twitch force was, at the same contraction level, 7.41+/-1.34 mN and 14.42+/-2.92 mN, for the FDI and ADM muscles, respectively. The peak-to-peak value of the MMG was significantly different for the same MU at different contraction levels, indicating a non-linear summation of the single MU contributions. For the FDI muscle, the MMG peak-to-peak value of individual MUs was 21.5+/-7.8 mm s(-2), when such MUs were activated with visual feedback provided to the subject, whereas, for the same MUs, it was 11.8+/-3.8 mm s(-2), when the subject maintained a constant force level of 2% MVC. The method proposed allows the non-invasive assessment of single MU membrane and contractile properties during voluntary contractions.
SourceAvailable from: Takanori Uchiyama[Show abstract] [Hide abstract]
ABSTRACT: The mechanomyogram from a single motor unit and the induced mechanomyogram at various levels of recruitment were measured with an acceleration sensor. The transfer functions between motor unit action potential (or electrical stimulation) and the mechanomyogram were identified using the singular value decomposition method. The purpose of this study is to clarify how the model order of the transfer function depends on the recruitment level. The second-to tenth-order transfer functions were calculated, and the difference between the observed and the estimated mechanomyograms using the transfer function, the fitness, was calculated. The relationship between the model order and the fitness was tested using the Holm-Bonferroni multiple comparison. At low levels (single motor unit, 20, and 40%) of recruitment, there were significant differences between the fourth-and higher-order models, but there were no significant differences between the fifth-and higher-order models. In contrast, at high levels (60, 80, and 100%) of recruitment, the fourth-order model did not show significant differences between the fifth-or higher-order models. As a result, the fifth-and fourth-order models were appropriate at low and high recruitment levels, respectively. The differences in the order might be caused by interactions between active and resting motor units.
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ABSTRACT: 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.Sensors 12/2014; 14(12):22940-22970. DOI:10.3390/s141222940 · 2.05 Impact Factor
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ABSTRACT: 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 continue 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 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.Clinical Biomechanics 06/2014; DOI:10.1016/j.clinbiomech.2014.04.003 · 1.88 Impact Factor