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(1) Background: The Modified Ashworth Scale (MAS) is commonly used clinically to evaluate spasticity, but its qualitative nature introduces subjectivity. We propose a novel metric scale to quantitatively measure spasticity using mechanomyography (MMG) to mitigate these subjective effects. (2) Methods: The flexor and extensor muscles of knee and elbow joints were assessed with the Modified Ashworth Scale (MAS) during the acquisition of mechanomyography (MMG) data. The median absolute amplitude of the MMG signals was utilized as a key descriptor. An algorithm was developed to normalize the MMG signals to a universal gravitational (G) acceleration scale, aligning them with the limits and range of MAS. (3) Results: We evaluated 34 lower and upper limbs from 22 volunteers (average age 39.91 ± 13.77 years) of both genders. Polynomial regression provided the best fit (R2 = 0.987), with negligible differences (mean of 0.001 G) between the MAS and MMG. We established three numerical sets for the median, minimum, and maximum MMG(G) values corresponding to each MAS range, ensuring consistent alignment of the Modified Ashworth levels with our proposed scale. (4) Conclusions: Muscle spasticity can now be quantitatively and semi-automatically evaluated using our algorithm and instrumentation, enhancing the objectivity and reliability of spasticity assessments.

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Mechanomyography (MMG) is an important muscle physiological activity signal that can reflect the amount of motor units recruited as well as the contraction frequency. As a result, MMG can be utilized to estimate the force produced by skeletal muscle. However, cross-talk and time-series correlation severely affect MMG signal recognition in the real world. These restrict the accuracy of dynamic muscle force estimation and their interaction ability in wearable devices. To address these issues, a hypothesis that the accuracy of knee dynamic extension force estimation can be improved by using MMG signals from a single muscle with less cross-talk is first proposed. The hypothesis is then confirmed using the estimation results from different muscle signal feature combinations. Finally, a novel model (improved grey wolf optimizer optimized long short-term memory networks, i.e., IGWO-LSTM) is proposed for further improving the performance of knee dynamic extension force estimation. The experimental results demonstrate that MMG signals from a single muscle with less cross-talk have a superior ability to estimate dynamic knee extension force. In addition, the proposed IGWO-LSTM provides the best performance metrics in comparison to other state-of-the-art models. Our research is expected to not only improve the understanding of the mechanisms of quadriceps contraction but also enhance the flexibility and interaction capabilities of future rehabilitation and assistive devices.

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Mechanomyography is currently the accepted laboratory reference standard for quantitative neuromuscular blockade monitoring. Mechanomyographs are not commercially available. Previously, a mechanomyograph was built by our laboratory and used in several clinical studies. It was subsequently redesigned to improve its usability and functionality and to accommodate a wider range of hand sizes and shapes using an iterative design process. Each version of the redesigned device was initially tested for usability and functionality in the lab with the investigators as subjects without electrical stimulation. The redesigned devices were then assessed on patients undergoing elective surgery under general anesthesia without neuromuscular blocking drugs. Since the patients were not paralyzed, the expected train-of-four ratio was 1.0. The device accuracy and precision were represented by the train-of-four ratio mean and standard deviation. If issues with the device's useability or functionality were discovered, changes were made, and the redesign processes repeated. The final mechanomyograph design was used to collect 2,362 train-of-four ratios from 21 patients. The mean and standard deviation of the train-of-four ratios were 0.99 ± 0.030. Additionally, the final mechanomyograph design was easier to use and adjust than the original design and fit a wider range of hand sizes. The final design also reduced the frequency of adjustments and the time needed for adjustments, facilitating data collection during a surgical procedure.

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In the present study, we used a transformer model and a fusion of biosignals to estimate rotational acceleration in elbow and shoulder joints. To achieve our study objectives, we proposed a mechanomyography (MMG) signal isolation technique based on a variational mode decomposition (VMD) algorithm. Our results show that the VMD algorithm delivered excellent performance in MMG signal extraction compared to the commonly used technique of empirical mode decomposition (EMD). In addition, we found that transformer models delivered estimates of joint acceleration that were more precise than those produced by mainstream time series forecasting models. The average R2 values of transformer are 0.967, 0.968, and 0.935, respectively. Finally, we found that using a fusion of signals resulted in more precise estimation performance compared to using MMG signals alone. The differences between the average R2 values are 0.041, 0.053, and 0.043, respectively. Taken together, the VMD isolation method, the transformer algorithm and the signal fusion technique described in this paper can be seen as supplying a robust framework for estimating rotational acceleration in upper-limb joints. Further study is warranted to examine the effectiveness of this framework in other musculoskeletal contexts.

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Motion recognition (MR)-based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human-computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non-intrusive muscle-sensing wearable device, that in conjunction with machine learning, enables motion-control-based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16-channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower-limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle-sensing-based somatosensory interaction, using the proposed wearable device, for enabling the real-time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography-based methods for achieving accurate human motion capture, which can further broaden the applications of motion-interactive wearable devices for the coming metaverse age.

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On the basis of extracting mechanomyography (MMG) signal features, the classification of hand movements has certain application values in human-machine interaction systems and wearable devices. In this paper, pattern recognition of hand movements based on MMG signal is studied with swarm intelligence algorithms introduced to optimize support vector machine (SVM). Time domain (TD) features, wavelet packet node energy (WPNE) features, frequency domain (FD) features, convolution neural network (CNN) features were extracted from each channel to constitute different feature sets. Three novel swarm intelligence algorithms (i.e., bald eagle search (BES), sparrow search algorithm (SSA), grey wolf optimization (GWO)) optimized SVM is proposed to train the models and recognition of hand movements are tested for each MMG feature extraction method. Using GWO as the optimization algorithm, time consumption is less than using the other two swarm algorithms. Using GWO with TD+FD features can obtain the classification accuracy of 93.55 %, which is higher than other methods while using CNN to extract features can be independent of domain knowledge. The results confirm GWO-SVM with TD + FD features is superior to some other methods in the classification problem for tiny samples based on MMG.

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Transcutaneous spinal cord stimulation (tSCS) provides a promising therapy option for individuals with injured spinal cords and multiple sclerosis patients with spasticity and gait deficits. Before the therapy, the examiner determines a suitable electrode position and stimulation current for a controlled application. For that, amplitude characteristics of posterior root muscle (PRM) responses in the electromyography (EMG) of the legs to double pulses are examined. This laborious procedure holds potential for simplification due to time-consuming skin preparation, sensor placement, and required expert knowledge. Here, we investigate mechanomyography (MMG) that employs accelerometers instead of EMGs to assess muscle activity. A supervised machine-learning classification approach was implemented to classify the acceleration data into no activity and muscular/reflex responses, considering the EMG responses as ground truth. The acceleration-based calibration procedure achieved a mean accuracy of up to 87% relative to the classical EMG approach as ground truth on a combined cohort of 11 healthy subjects and 11 patients. Based on this classification, the identified current amplitude for the tSCS therapy was in 85%, comparable to the EMG-based ground truth. In healthy subjects, where both therapy current and position have been identified, 91% of the outcome matched well with the EMG approach. We conclude that MMG has the potential to make the tuning of tSCS feasible in clinical practice and even in home use.

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PURPOSE: The purpose of this study was to compare the Stimpod electromyograph neuromuscular blockade monitor to mechanomyography, which is widely considered to be the reference standard. METHODS: The Stimpod electromyograph was used with its designated electrode array on the same hand as the mechanomyograph. Pairs of train-of-four measurements were recorded every 0.5-2 min. When the train-of-four count was zero on the electromyograph monitor, pairs of post tetanic count measurements were recorded every 2.5 min, instead of train-of-four measurements. Measurements were recorded from immediately after induction of anesthesia until just before emergence. Stimulation current was set to 60 mA with a duration of 200 microsec. The mechanomyography recording system recorded each twitch waveform for analysis. High resolution electromyograph waveforms were also recorded using a datalogger accessory provided by the manufacturer, facilitating inspection of individual waveforms. The administration of neuromuscular blocking drugs was left up to the discretion of the anesthesia care team. RESULTS: Twenty-three patients contributed 1,088 data pairs suitable for analysis. Bland-Altman analysis of 415 pairs of train-of-four ratios showed a bias of 0.028 and limits of agreement of -0.18 and 0.24. Two hundred seventy-three train-of-four count data pairs were compared by Cohen's quadratically weighted kappa which was calculated to be 0.44, indicating moderate agreement. Three hundred thirty-eight post tetanic count data pairs were compared by Cohen's quadradically weighted kappa which was calculated to be 0.80, indicating substantial agreement. CONCLUSION: The electromyograph produced results that were comparable to the mechanomyograph.

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Flexible piezoresistive sensors built by printing nanoparticles onto soft substrates are crucial for continuous health monitoring and wearable devices. In this study, a mechanomyography (MMG) sensor was developed using a flexible piezoresistive MMG signal sensor based on a pyramidal polydimethylsiloxane (PDMS) microarray sprayed with carbon nanotubes (CNTs). The experiment was conducted, and the results show that the sensitivity of the sensor can reach 0.4 kPa-1 in the measurement range of 0~1.5 kPa, and the correlation reached 96%. This has further implications for the possibility that muscle activation can be converted into mechanical movement. The integrity of the sensor in terms of its MMG signal acquisition was tested based on five subjects who were performing arm bending and arm extending movements. The results of this test were promising.

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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.

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Neuromuscular electrical stimulation plays a pivotal role in rehabilitating muscle function among individuals with neurological impairment. However, there remains uncertainty regarding whether the muscle's response to electrical excitation is affected by forearm posture, joint angle, or a combination of both factors. This study aimed to investigate the effects of forearm postures and elbow joint angles on the muscle torque and MMG signals. Measurements of the torque around the elbow and MMG of the biceps brachii (BB) muscle were conducted in 36 healthy subjects (age, 22.24 ± 2.94 years; height, 172 ± 0.5 cm; and weight, 67.01 ± 7.22 kg) using an in-house elbow flexion testbed and neuromuscular electrical stimulation (NMES) of the BB muscle. The BB muscle was stimulated while the forearm was positioned in the neutral, pronation, or supination positions. The elbow was flexed at angles of 10°, 30°, 60°, and 90°. The study analyzed the impact of the forearm posture(s) and elbow joint angle(s) on the root-mean-square value of the torque (TQRMS). Subsequently, various MMG parameters, such as the root-mean-square value (MMGRMS), the mean power frequency (MMGMPF), and the median frequency (MMGMDF), were analyzed along the longitudinal, lateral, and transverse axes of the BB muscle fibers. The test-retest interclass correlation coefficient (ICC21) for the torque and MMG ranged from 0.522 to 0.828. Repeated-measure ANOVAs showed that the forearm posture and elbow flexion angle significantly influenced the TQRMS (p < 0.05). Similarly, the MMGRMS, MMGMPF, and MMGMDF showed significant differences among all the postures and angles (p < 0.05). However, the combined main effect of the forearm posture and elbow joint angle was insignificant along the longitudinal axis (p > 0.05). The study also found that the MMGRMS and TQRMS increased with increases in the joint angle from 10° to 60° and decreased at greater angles. However, during this investigation, the MMGMPF and MMGMDF exhibited a consistent decrease in response to increases in the joint angle for the lateral and transverse axes of the BB muscle. These findings suggest 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 the muscle length. Because restoring the function of limbs is a common goal in rehabilitation services, the use of MMG in the development of methods that may enable the real-time tracking of exact muscle dimensional changes and activation levels is imperative.

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OBJECTIVES: To examine the effects of joint angle (JA) on maximal voluntary isometric contractions (MVIC) and neuromuscular responses following a sustained, isometric forearm flexion task anchored to a rating of perceived exertion (RPE) of 8 (RPE=8). METHODS: Nine women (age: 20.7±2.9 yrs; height: 168.8±7.2 cm; body mass: 66.3±6.8 kg) performed 2,3s forearm flexion MVICs at JAs of 75°, 100°, and 125° prior to and following a sustained, isometric forearm flexion task anchored to RPE=8 to task failure (torque reduced to zero) at JA100. Electromyographic (EMG) and mechanomyographic (MMG) signals were recorded from the biceps brachii. RESULTS: The MVIC at JA100 (collapsed across Time) was significantly greater (p<0.05) than JA75 and JA125. The pre-test MVIC was significantly greater (p<0.001) than the post-test. For EMG amplitude (AMP) and EMG mean power frequency (MPF), pre-test values were significantly greater (p<0.05) than the post-test values, with no differences between JAs. For MMG AMP and MMG MPF, there were no significant (p>0.05) differences between Time or JAs. Pre-test neuromuscular efficiency (normalized MVIC/normalized EMG AMP) was significantly greater (p=0.005) than post-test. CONCLUSION: Following a sustained, isometric forearm flexion task anchored to RPE=8 at JA100, the fatigue-induced MVIC and neuromuscular responses were not affected by JA.

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The purpose of this study was to compare electromyographic (EMG) with mechanomyographic (MMG) recordings during isometric conditions, and during a simulated load-lifting task. Twenty-two males (age: 25.5 ± 5.3 years) first performed maximal voluntary contractions (MVC) and submaximal isometric contractions of upper limb muscles at 25%, 50% and 75% MVC. Participants then executed repetitions of a functional activity simulating a load-lifting task above shoulder level, at 25%, 50% and 75% of their maximum activity (based on MVC). The low-frequency part of the accelerometer signal (<5 Hz) was used to segment the six phases of the motion. EMG and MMG were both recorded during the entire experimental procedure. Root mean square (RMS) and mean power frequency (MPF) were selected as signal extraction features. During isometric contractions, EMG and MMG exhibited similar repeatability scores. They also shared similar RMS vs. force relationship, with RMS increasing to 75% MVC and plateauing to 100%. MPF decreased with increasing force to 75% MVC. In dynamic condition, RMSMMG exhibited higher sensitivity to changes in load than RMSEMG. These results confirm the feasibility of MMG measurements to be used during functional activities outside the laboratory. It opens new perspectives for future applications in sports science, ergonomics and human-machine interface conception.

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Pattern recognition of lower-limb movements based on mechanomyography (MMG) signals has a certain application value in the study of wearable rehabilitation-training devices. In this paper, MMG feature selection methods based on a chameleon swarm algorithm (CSA) and a grasshopper optimization algorithm (GOA) are proposed for the pattern recognition of knee and ankle movements in the sitting and standing positions. Wireless multichannel MMG acquisition systems were designed and used to collect MMG movements from four sites on the subjects thighs. The relationship between the threshold values and classification accuracy was analyzed, and comparatively high recognition rates were obtained after redundant information was eliminated. When the threshold value rose, the recognition rates from the CSA fluctuated within a small range: up to 88.17% (sitting position) and 90.07% (standing position). However, the recognition rates from the GOA drop dramatically when increasing the threshold value. The comparison results demonstrated that using a GOA consumes less time and selects fewer features, while a CSA gives higher recognition rates of knee and ankle movements.

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This study examined the effects of joint angle (JA) on maximal voluntary isometric contraction (MVIC) and neuromuscular responses following fatiguing tasks anchored to RPE. Nine men (mean ± SD: age = 20.7 ± 1.2 yrs) performed forearm flexion MVICs at elbow JAs of 75° and 125° before and after sustained, isometric forearm flexion tasks to failure at fatiguing joint angles (FJA) of 75° and 125° anchored to RPE = 8. The amplitude and frequency of the electromyographic and mechanomyographic signals were recorded. Neuromuscular efficiency was calculated by dividing normalized torque by normalized electromyographic amplitude. A dependent t-test was used to assess the mean difference for time to task failure (TTF) between FJA. Repeated measure ANOVAs were used to assess mean differences for pre-test to post-test MVIC and neuromuscular responses. There was no significant difference between FJA for TTF (p = 0.223). The MVIC (collapsed across FJA and MVIC JA) decreased from pre-test to post-test (51.1 ± 5.0 vs. 45.3 ± 5.6 Nm, p < 0.001). Normalized neuromuscular parameters remained unchanged (p > 0.05). The FJA resulted in similar torque and neuromuscular responses, and the decreases in MVIC were not tracked by changes in the neuromuscular parameters. Thus, the neuromuscular parameters were not sensitive to fatigue, and pre-test to post-test measures may be compared between different FJA.

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Electromyography (EMG) is the clinical standard for capturing muscle activation data to gain insight into neuromuscular control, yet challenges surrounding data analysis limit its use during dynamic tasks. Surface mechanomyography (sMMG) sensors are novel wearable devices that measure the physical output of muscle excursion during contraction, which may offer potential easy application to assess neuromuscular control. This study aimed to investigate sMMG detection of the timing patterns of muscle contraction compared to EMG. Fifteen healthy participants (mean age = 31.7 ± 9.1 y; eight males and seven females) were donned with EMG and sMMG sensors on their right quadriceps for simultaneous data capture during bilateral deep squats, and a subset performed three sets of repeated unilateral partial squats. No significant difference in the total duration of contraction was detected by EMG and sMMG during bilateral (p = 0.822) and partial (p = 0.246) squats. sMMG and EMG timing did not differ significantly for eccentric (p = 0.414) and concentric (p = 0.462) phases of muscle contraction during bilateral squats. The sMMG magnitude of quadriceps excursion demonstrated excellent intra-session retest reliability for bilateral (ICC3,1 = 0.962 mm) and partial (ICC3,1 = 0.936 mm, n = 10) squats. The sMMG sensors accurately and consistently provided key quadriceps muscle performance metrics during two physical activities commonly used to assess neuromuscular control for injury prevention, rehabilitation, and exercise training.

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Falls and fall-related injuries are significant public health problems in older adults. While balance-controlling strategies have been extensively researched, there is still a lack of understanding regarding how fast the lower-limb muscles contract and coordinate in response to a sudden loss of standing balance. Therefore, this pilot study aims to investigate the speed and timing patterns of multiple joint/muscles' activities among the different challenges in standing balance. Twelve healthy young subjects were recruited, and they received unexpected translational balance perturbations with randomized intensities and directions. Electromyographical (EMG) and mechanomyographical (MMG) signals of eight dominant-leg's muscles, dominant-leg's three-dimensional (3D) hip/knee/ankle joint angles, and 3D postural sways were concurrently collected. Two-way ANOVAs were used to examine the difference in timing and speed of the collected signals among muscles/joint motions and among perturbation intensities. This study has found that (1) agonist muscles resisting the induced postural sway tended to activate more rapidly than the antagonist muscles, and ankle muscles contributed the most with the fastest rate of response; (2) voluntary corrective lower-limb joint motions and postural sways could occur as early as the perturbation-induced passive ones; (3) muscles reacted more rapidly under a larger perturbation intensity, while the joint motions or postural sways did not. These findings expand the current knowledge on standing-balance-controlling mechanisms and may potentially provide more insights for developing future fall-prevention strategies in daily life.

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Purpose: To examine the effects of a 5-week continuous cycling training intervention on electromyographic amplitude (EMGRMS)- and mechanomyographic amplitude (MMGRMS)-torque relationships of the vastus lateralis (VL) during a prolonged contraction. Methods: Twenty-four sedentary, young adults performed maximal voluntary contractions (MVCs) and a prolonged isometric trapezoidal contraction at the same absolute 40% MVC for the knee extensors before (PRE) and after training (POSTABS). Individual b- (slopes) and a-terms (y-intercepts) were calculated from the log-transformed electromyographic amplitude (EMGRMS)- and mechanomyographic amplitude (MMGRMS)-torque relationships during the increasing and decreasing segments of the trapezoid. EMGRMS and MMGRMS was normalized for the 45-s steady torque segment. Results: At PRE, b-terms for the EMGRMS-torque relationships during the linearly decreasing segment were greater than the increasing segment (p < .001), and decreased from PRE to POSTABS (p = .027). a-terms were greater during the linearly increasing than decreasing segment at PRE, while the a-terms for the linearly decreasing segment increased from PRE to POSTABS (p = .027). For the MMGRMS-torque relationships, b-terms during the linearly decreasing segment decreased from PRE to POSTABS (p = .013), while a-terms increased from PRE to POSTABS when collapsed across segments (p = .022). Steady torque EMGRMS increased for POSTABS (p < .001). Conclusion: Although cycling training increased aerobic endurance, incorporating resistance training may benefit athletes/individuals as the alterations in neuromuscular parameters post-training suggest a greater neural cost (EMGRMS) and mechanical output (MMGRMS) to complete the same pre-training fatiguing contraction.

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This study examined potential sex-related differences and correlations among the pennation angle (PA), muscle thickness (MT), and mechanomyographic amplitude (MMGRMS)-torque relationships of the vastus lateralis (VL) in 11 healthy males and 12 healthy females. The PA and MT of the VL were quantified with ultrasound. Participants performed an isometric muscle action of the knee extensors that linearly increased to 70% of maximal strength followed by a 12 s plateau. MMG was recorded from the VL. Linear regression models were fit to the log-transformed MMGRMS-torque relationships to calculate b terms (slopes) for the linearly increasing segment. MMGRMS was averaged during the plateau. Males exhibited greater PA (p < 0.001), MT (p = 0.027), b terms (p = 0.005), and MMGRMS (p = 0.016). The b terms were strongly (p < 0.001, r = 0.772) and moderately correlated (p = 0.004, r = 0.571) with PA and MT, respectively, while MMGRMS was moderately correlated with PA (p = 0.018, r = 0.500) and MT (p = 0.014, r = 0.515). The greater mechanical behavior of individuals possessing a larger PA and MT of the VL may reflect increased cross-bridge activity within the muscle fibers. Additionally, PA may help explain sex-related differences in MMGRMS between sexes.

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Introduction: An electrically stimulated intermittent fatigue test using mechanomyography was recently proposed as a possible tool for detecting clinically relevant changes in muscle function. This study was designed to determine whether the proposed test can detect additional fatigue when it should be present. Methods: Subjects (n = 10) underwent two trials each (occluded and normal blood flow) with a standardized fatigue protocol on the Ankle Dorsiflexors (AD) and Wrist Extensors (WE) using a clinical electrical stimulator. Results: Mean normalized twitch acceleration was strongly predictive of mean normalized torque (R 2 = 0.828). The WE experienced lower twitch magnitudes throughout the tourniquet trial (10.81 ± 1.25 m/s2) compared to normal blood flow (18.05 ± 1.06 m/s2). The AD twitches were overall reduced in the tourniquet trial (3.87 ± 0.48 m/s2) compared with the control trial (8.57 ± 0.91 m/s2). Conclusion: Occluding blood flow to a muscle should cause greater muscle fatigue. The ability to detect reduced contraction magnitudes during an electrically stimulated fatigue protocol resulting from low blood flow suggests the proposed test may be capable of detecting clinically relevant muscle deficits.