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 for neuromuscular electrical stimulation feedback applications in persons with spinal cord injury / Ibitoye Morufu Olusola

Neuromuscular Electrical Stimulation (NMES)-evoked muscle contractions confers therapeutic and functional gains on persons with Spinal Cord Injury (SCI). However, the optimal efficacy of commercial NMES systems’ application is inhibited by the imprecision in muscle force/torque production and rapid muscle fatigue. Evidence suggests that the application of a muscle mechanical response (force/torque) as a feedback to modulate the administration of NMES could optimize the efficacy of the technology by enabling muscle force regulation, and delaying the onset of muscle fatigue. Currently, a direct muscle force measurement is impractical and there is also lack of a reliable, electrical stimulus artifact-free and non-invasive proxy of muscle force to drive the NMES systems for enhanced controllability and clinical use. Attempts on the application of evoked-electromyography for this purpose remain debatable and clinically limited. As a viable alternative, this thesis proposes a non-invasive muscle force/torque measurement technique based on the mechanical activity of contracting muscles (Mechanomyography or MMG). This investigation was motivated by the knowledge that mechanomyography is immune from certain limitations of evoked-electromyography and provides direct information on muscle’s mechanical responses to the electrical stimulation. Systematic literature survey revealed a lack of clear understanding of the relationship between mechanomyography and NMES-evoked torque production in a paralyzed muscle. Therefore, the present research introduces mechanomyography as a proxy of NMES-evoked torque in persons with SCI. At the outset, a hybrid procedure was developed to establish mechanomyography as a proxy of muscle force/torque in healthy volunteers and persons with SCI. This was used to investigate the pattern of incremental torque production and subsequently facilitated the estimation of the torque from mechanomyography using a computational intelligent technique based on Support Vector Regression (SVR) modelling. This thesis also demonstrated, in a clinical setting, the validity of the mechanomyography as a relevant parameter for studying muscle fatigue during critical knee buckling stress i.e. standing-to-failure challenge in persons with SCI. Due to the peculiarity of the study participants/target population and the intended clinical application of NMES-supported standing, the quadriceps muscle group, widely reported for its relevance in studying the knee torque dynamics, was selected as the study site. Findings from these studies revealed that the mechanomyographic amplitude is highly correlated (r> 0.95; P 0.05) of the mechanomyography during force production might be useful to evaluate the recovery or deterioration of motor unit activities following NMES supported exercise and as an alternative technique for monitoring the NMES-evoked muscle activity for practical control applications. Together, this thesis lays a foundation for the future implementation of MMG-driven NMES technologies

Strategies for Rapid Muscle Fatigue Reduction during FES Exercise in Individuals with Spinal Cord Injury: A Systematic Review.

BACKGROUND:Rapid muscle fatigue during functional electrical stimulation (FES)-evoked muscle contractions in individuals with spinal cord injury (SCI) is a significant limitation to attaining health benefits of FES-exercise. Delaying the onset of muscle fatigue is often cited as an important goal linked to FES clinical efficacy. Although the basic concept of fatigue-resistance has a long history, recent advances in biomedical engineering, physiotherapy and clinical exercise science have achieved improved clinical benefits, especially for reducing muscle fatigue during FES-exercise. This review evaluated the methodological quality of strategies underlying muscle fatigue-resistance that have been used to optimize FES therapeutic approaches. The review also sought to synthesize the effectiveness of these strategies for persons with SCI in order to establish their functional impacts and clinical relevance. METHODS:Published scientific literature pertaining to the reduction of FES-induced muscle fatigue was identified through searches of the following databases: Science Direct, Medline, IEEE Xplore, SpringerLink, PubMed and Nature, from the earliest returned record until June 2015. Titles and abstracts were screened to obtain 35 studies that met the inclusion criteria for this systematic review. RESULTS:Following the evaluation of methodological quality (mean (SD), 50 (6) %) of the reviewed studies using the Downs and Black scale, the largest treatment effects reported to reduce muscle fatigue mainly investigated isometric contractions of limited functional and clinical relevance (n = 28). Some investigations (n = 13) lacked randomisation, while others were characterised by small sample sizes with low statistical power. Nevertheless, the clinical significance of emerging trends to improve fatigue-resistance during FES included (i) optimizing electrode positioning, (ii) fine-tuning of stimulation patterns and other FES parameters, (iii) adjustments to the mode and frequency of exercise training, and (iv) biofeedback-assisted FES-exercise to promote selective recruitment of fatigue-resistant motor units. CONCLUSION:Although the need for further in-depth clinical trials (especially RCTs) was clearly warranted to establish external validity of outcomes, current evidence was sufficient to support the validity of certain techniques for rapid fatigue-reduction in order to promote FES therapy as an integral part of SCI rehabilitation. It is anticipated that this information will be valuable to clinicians and other allied health professionals administering FES as a treatment option in rehabilitation and aid the development of effective rehabilitation interventions

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

PRISMA flow chart for included and excluded studies in the systematic review on fatigue reduction strategies during FES exercise.

<p>PRISMA flow chart for included and excluded studies in the systematic review on fatigue reduction strategies during FES exercise.</p