This research has proved that mechanomyographic (MMG) signals can be used for evaluating muscle performance. Stimulation of the lost physiological functions of a muscle using an electrical signal has been determined crucial in clinical and experimental settings in which voluntary contraction fails in stimulating specific muscles. Previous studies have already indicated that characterizing contractile properties of muscles using MMG through neuromuscular electrical stimulation (NMES) showed excellent reliability. Thus, this review highlights the use of MMG signals on evaluating skeletal muscles under electrical stimulation. In total, 336 original articles were identified from the Scopus and SpringerLink electronic databases using search keywords for studies published between 2000 and 2020, and their eligibility for inclusion in this review has been screened using various inclusion criteria. After screening, 62 studies remained for analysis, with two additional articles from the bibliography, were categorized into the following: (1) fatigue, (2) torque, (3) force, (4) stiffness, (5) electrode development, (6) reliability of MMG and NMES approaches, and (7) validation of these techniques in clinical monitoring. This review has found that MMG through NMES provides feature factors for muscle activity assessment, highlighting standardized electromyostimulation and MMG parameters from different experimental protocols. Despite the evidence of mathematical computations in quantifying MMG along with NMES, the requirement of the processing speed, and fluctuation of MMG signals influence the technique to be prone to errors. Interestingly, although this review does not focus on machine learning, there are only few studies that have adopted it as an alternative to statistical analysis in the assessment of muscle fatigue, torque, and force. The results confirm the need for further investigation on the use of sophisticated computations of features of MMG signals from electrically stimulated muscles in muscle function assessment and assistive technology such as prosthetics control.
This study aimed to investigate the effects of forearm postures and elbow joint angles on muscle torque and mechanomyography (MMG) signals. The torque about the elbow and MMG of the biceps brachii (BB) muscle were measured in 36 healthy subjects using an in-house elbow flexion testbed and Neuromuscular electrical stimulation (NMES) of the BB muscle. The BB muscle received stimulation intensity while the forearm was positioned in the neutral, pronation or supination. The elbow was flexed at angles 10°, 30°, 60° and 90°. (0° = full elbow extension). The study analyzed the impact of forearm posture(s) and elbow joint angle(s) on the root mean square value of torque (TQ_RMS). Subsequently, MMG parameters such as the root mean square value (MMG_RMS), the mean power frequency (MMG_MPF), and the median frequency (MMG_MDF), were analyzed in the longitudinal, lateral, and transverse axes of the BB muscle fibers. Forearm posture and elbow flexion angle were found to significantly influence TQ_RMS (P < 0.05). Similarly, MMG_RMS, MMG_MPF and MMG_MDF exhibited a significant difference for all postures and angles (P < 0.05). However, the combined main effect of forearm postures and elbow joint angles was insignificant in the longitudinal axis (P > 0.05). The study also found that MMG_RMS and TQ_RMS increased with the joint angle to 60° and decreased to the other angle(s). However, during this investigation,MMG_MPF and MMG_MDF exhibited a consistent decrease in response to joint angles. This finding suggests that the muscle contraction evoked by NMES may be influenced by the interplay between actin and myosin filaments, which are responsible for muscle contraction and are in turn influenced by muscle length. As restoring the function of limbs is a common goal in rehabilitation services, it becomes imperative to develop methods that may enable the real-time tracking of exact muscle dimensional changes and activation levels.
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